Setup¶
Install in R:
install.packages("BiocManager")
BiocManager::install(c("edgeR", "BiocParallel", "RhpcBLASctl"))
Install
pip install toytree ete4 pybiomart
Options & Data¶
In [ ]:
%load_ext autoreload
%autoreload 2
import os
import re
import pickle
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.sparse import issparse
from pymer4.models import lmer, lm, glmer, compare
from pymer4 import config
import polars
import statsmodels.api as sm
import senepy
import gseapy as gp
import pertpy as pt
import scanpy as sc
import pandas as pd
import numpy as np
import scflow
from scflow.ax import find_senescence_genes, run_senepy
os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
config.PANDAS_BACKEND = True
pd.set_option("display.max_rows", 500) # or None for unlimited rows
pd.set_option("display.max_columns", 100)
pd.set_option("display.max_colwidth", 100)
pd.set_option("display.width", 300)
ncpus = os.cpu_count() - 1
overwrite = True
# If You Want Results Emailed (Will Overwrite HTML Regardless of `overwite`)
cur_file = os.path.join(os.path.abspath(""), "analyze_senescence_celltype"
"_specific_threshold.ipynb")
html_out = os.path.splitext(cur_file)[0] + ".html"
email = "elizabeth.aslinger@aya.yale.edu"
# set email to None to skip
# Cell Type Label Column
col_celltype = "annotation_by_overlap"
# col_celltype = "Annotation_ToppGene"
# col_celltype = "cellmap_class_name"
# col_celltype = "cellmap_class_name_collapsed"
# use_hierarchy = False
use_hierarchy = True # collapse neuron categories?
# Metadata Information
species = "Mouse"
col_sample, col_batch = "sample", "Group"
# col_age = "Characteristics[Age at Euthanasia]"
# col_condition = "Factor Value[Spaceflight]"
col_age = "Age_End"
col_condition = "Condition"
group_order = ["Ground Control | 12 Weeks", "Ground Control | 29 Weeks",
"Space Flight | 12 Weeks", "Space Flight | 29 Weeks"]
# group_order = ["Ground Control | 20 Weeks", "Ground Control | 37 Weeks",
# "Space Flight | 20 Weeks", "Space Flight | 37 Weeks"]
keys = {col_condition: dict(key_control="Ground Control",
key_treatment="Space Flight"),
# col_age: dict(key_control="20 Weeks",
# key_treatment="37 Weeks"),
col_age: dict(key_control="12 Weeks",
key_treatment="29 Weeks"),
col_batch: dict(key_control=group_order[0],
key_treatment=group_order[1:])}
palette = {col_condition: {keys[col_condition]["key_control"]: "b",
keys[col_condition]["key_treatment"]: "r"},
col_age: {keys[col_age]["key_control"]: "g",
keys[col_age]["key_treatment"]: "y"},
col_batch: dict(zip(group_order, ["c", "b", "tab:pink", "r"]))}
# Scoring Metrics
sen_metrics = ["senmayo"]
# sen_metrics = ["senepy", "senmayo"]
use_metric = "senmayo"
percentile = 1
# File Input & Output
file_path = "data/OSD-613_integrated.h5ad"
file_path_new = os.path.splitext(file_path)[0] + "_analyzed.h5ad"
# Load Data
self = scflow.Rna(file_path, col_sample=col_sample, col_batch=col_batch,
col_celltype=col_celltype) # data
self.rna.obs.loc[:, col_batch] = self.rna.obs[col_batch].astype(
pd.CategoricalDtype(categories=group_order, ordered=True))
self.rna.obs.loc[:, "Spaceflight"] = (self.rna.obs[col_condition] == keys[
col_condition]["key_treatment"]).astype(int) # 1 vs. 0 spaceflight
self.rna.obs.loc[:, "Aged"] = (self.rna.obs[col_age] == keys[
col_age]["key_treatment"]).astype(int) # 1 vs. 0 older
self.rna = self.rna[:, self.rna.var.mt == False] # snRNA-seq so remove MT-
self.rna = self.rna[:, self.rna.var.ribo == False] # snRNA-seq so remove RB-
# Hierarchical
keys_cts = {"Neuron": ["Excitatory", "Inhibitory", "Excitatory-Inhibitory"],
"Macroglia": ["Oligodendrocyte", "OPC", "Astrocyte"],
"Microglia": ["Microglial"]}
key_cts = dict(pd.concat([pd.Series(dict(([(i, x) for i in keys_cts[
x]]))) for x in keys_cts]))
self.rna.obs.loc[:, f"{col_celltype}_hierarchy"] = self.rna.obs[
col_celltype].replace(key_cts)
if use_hierarchy is True:
col_celltype = f"{col_celltype}_hierarchy"
self.col_celltype = col_celltype
self.rna.raw = None
# Display Object
print(self.rna)
print(self.rna.obs.groupby(col_sample).describe())
self.rna.obs
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/senepy/load_hubs.py:3: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. from pkg_resources import resource_filename /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/marshmallow/__init__.py:17: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead. __version_info__ = tuple(LooseVersion(__version__).version) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/marshmallow/fields.py:198: RemovedInMarshmallow4Warning: Passing field metadata as a keyword arg is deprecated. Use the explicit `metadata=...` argument instead. warnings.warn(
AnnData object with n_obs × n_vars = 103274 × 12097
obs: 'Group', 'sample', 'Characteristics[Organism]', 'Term Source REF', 'Term Accession Number', 'Characteristics[Strain]', 'Term Source REF.1', 'Term Accession Number.1', 'Characteristics[Genotype]', 'Term Source REF.2', 'Term Accession Number.2', 'Characteristics[Animal Source]', 'Characteristics[Sex]', 'Term Source REF.3', 'Term Accession Number.3', 'Factor Value[Spaceflight]', 'Term Source REF.4', 'Term Accession Number.4', 'Factor Value[Age]', 'Unit', 'Term Source REF.5', 'Term Accession Number.5', 'Characteristics[Material Type]', 'Term Source REF.6', 'Term Accession Number.6', 'Characteristics[diet]', 'Characteristics[Feeding Schedule]', 'Characteristics[Age at Euthanasia]', 'Unit.1', 'Term Source REF.7', 'Term Accession Number.7', 'Protocol REF', 'Parameter Value[habitat]', 'Parameter Value[Enrichment material]', 'Parameter Value[duration]', 'Unit.2', 'Term Source REF.8', 'Term Accession Number.8', 'Parameter Value[light cycle]', 'Protocol REF.1', 'Parameter Value[Euthanasia Method]', 'Parameter Value[Sample Preservation Method]', 'Term Source REF.9', 'Term Accession Number.9', 'Parameter Value[Sample Storage Temperature]', 'Unit.3', 'Term Source REF.10', 'Term Accession Number.10', 'Comment[RFID]', 'Comment[Euthanasia Date]', 'n_cells_original_sample', 'min_max_genes', 'min_max_cells', 'max_mt', 'min_max_counts', 'zero_center', 'max_value', 'doublet_detection', 'n_genes_by_counts', 'total_counts', 'log1p_n_genes_by_counts', 'log1p_total_counts', 'total_counts_mt', 'pct_counts_mt', 'log1p_total_counts_mt', 'total_counts_ribo', 'pct_counts_ribo', 'log1p_total_counts_ribo', 'total_counts_hb', 'pct_counts_hb', 'log1p_total_counts_hb', 'n_counts', 'n_genes', 'doublet_score', 'predicted_doublet', 'resolution_individual', 'min_dist_individual', 'leiden_individual', 'annotation_by_markers_individual', 'annotation_by_markers_individual_heterogeneous_collapsed', '_scvi_batch', '_scvi_labels', 'annotation_scanvi', 'kws_integrate', 'leiden', 'leiden_resolution', 'leiden_n_neighbors', 'leiden_min_dist', 'annotation_by_overlap', 'annotation_toppgene', 'predicted_labels', 'majority_voting', 'majority_voting_probabilities', 'majority_voting_short', 'annotation_majority_voting', 'Age_Start', 'Age_End', 'Condition', 'Spaceflight', 'Aged', 'annotation_by_overlap_hierarchy'
var: 'mt', 'ribo', 'hb', 'n_cells_by_counts', 'total_counts', 'mean_counts', 'pct_dropout_by_counts', 'log1p_total_counts', 'log1p_mean_counts'
uns: 'Group_colors', '_scvi_manager_uuid', '_scvi_uuid', 'annotation_by_overlap_colors', 'annotation_majority_voting_colors', 'annotation_toppgene_colors', 'leiden', 'leiden_colors', 'neighbors', 'over_clustering', 'rank_genes_groups_leiden', 'sample_colors', 'umap'
obsm: 'X_pca', 'X_pca_old', 'X_scANVI', 'X_scVI', 'X_umap', '_scvi_extra_categorical_covs'
layers: 'counts', 'log1p', 'scaled'
obsp: 'connectivities', 'distances'
Factor Value[Age] Characteristics[Age at Euthanasia] Parameter Value[duration] Parameter Value[Sample Storage Temperature] \
count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25%
sample
RRRM2_BRN_GC_ISS-T_YNG_GY4 7547.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 7547.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 7547.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 7547.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_YNG_GY9 5465.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 5465.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 5465.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 5465.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_OLD_GO18 5842.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 5842.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 5842.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 5842.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 4922.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 4922.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 4922.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 4922.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_OLD_GO19 7539.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 7539.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 7539.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 7539.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_OLD_GO13 3595.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 3595.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 3595.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 3595.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 5095.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 5095.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 5095.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 5095.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 3669.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 3669.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 3669.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 3669.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 5747.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 5747.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 5747.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 5747.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_YNG_GY7 5265.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 5265.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 5265.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 5265.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 3864.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 3864.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 3864.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 3864.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_YNG_GY1 3672.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 3672.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 3672.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 3672.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 3730.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 3730.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 3730.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 3730.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 1321.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 1321.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 1321.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 1321.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_OLD_GO16 18386.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 18386.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 18386.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 18386.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 4050.0 29.0 0.0 29.0 29.0 29.0 29.0 29.0 4050.0 37.0 0.0 37.0 37.0 37.0 37.0 37.0 4050.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 4050.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_GC_ISS-T_YNG_GY2 9651.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 9651.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 9651.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 9651.0 -80.0 0.0 -80.0 -80.0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 3914.0 12.0 0.0 12.0 12.0 12.0 12.0 12.0 3914.0 20.0 0.0 20.0 20.0 20.0 20.0 20.0 3914.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 3914.0 -80.0 0.0 -80.0 -80.0
n_cells_original_sample n_genes_by_counts total_counts ... _scvi_labels leiden_resolution \
50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean ... 75% max count mean
sample ...
RRRM2_BRN_GC_ISS-T_YNG_GY4 -80.0 -80.0 -80.0 7547.0 8542.0 0.0 8542.0 8542.0 8542.0 8542.0 8542.0 7547.0 1464.316152 814.740976 299.0 695.00 1367.0 2129.00 3735.0 7547.0 2414.633545 ... 0.0 0.0 7547.0 0.027
RRRM2_BRN_GC_ISS-T_YNG_GY9 -80.0 -80.0 -80.0 5465.0 6044.0 0.0 6044.0 6044.0 6044.0 6044.0 6044.0 5465.0 2703.472827 1308.060665 651.0 1328.00 2972.0 3683.00 5979.0 5465.0 3206.402588 ... 0.0 0.0 5465.0 0.027
RRRM2_BRN_GC_ISS-T_OLD_GO18 -80.0 -80.0 -80.0 5842.0 6503.0 0.0 6503.0 6503.0 6503.0 6503.0 6503.0 5842.0 1678.743923 875.860511 283.0 828.00 1768.0 2364.75 3987.0 5842.0 2547.017334 ... 0.0 0.0 5842.0 0.027
RRRM2_BRN_FLT_ISS-T_OLD_FO20 -80.0 -80.0 -80.0 4922.0 5641.0 0.0 5641.0 5641.0 5641.0 5641.0 5641.0 4922.0 2467.499187 1334.135315 554.0 1044.25 2718.0 3561.75 5628.0 4922.0 3051.313232 ... 0.0 0.0 4922.0 0.027
RRRM2_BRN_GC_ISS-T_OLD_GO19 -80.0 -80.0 -80.0 7539.0 8476.0 0.0 8476.0 8476.0 8476.0 8476.0 8476.0 7539.0 2632.348985 1279.054702 566.0 1238.00 2939.0 3594.50 5858.0 7539.0 3189.832764 ... 0.0 0.0 7539.0 0.027
RRRM2_BRN_GC_ISS-T_OLD_GO13 -80.0 -80.0 -80.0 3595.0 3880.0 0.0 3880.0 3880.0 3880.0 3880.0 3880.0 3595.0 2588.294298 1327.554343 776.0 1262.00 2583.0 3736.50 5590.0 3595.0 3141.175537 ... 0.0 0.0 3595.0 0.027
RRRM2_BRN_FLT_ISS-T_YNG_FY8 -80.0 -80.0 -80.0 5095.0 5658.0 0.0 5658.0 5658.0 5658.0 5658.0 5658.0 5095.0 2789.168008 1592.824136 466.0 1131.00 3043.0 4103.00 6776.0 5095.0 3192.124023 ... 0.0 0.0 5095.0 0.027
RRRM2_BRN_FLT_ISS-T_YNG_FY7 -80.0 -80.0 -80.0 3669.0 4238.0 0.0 4238.0 4238.0 4238.0 4238.0 4238.0 3669.0 1567.765604 1026.974124 314.0 547.00 1545.0 2370.00 4581.0 3669.0 2420.943115 ... 0.0 0.0 3669.0 0.027
RRRM2_BRN_FLT_ISS-T_OLD_FO19 -80.0 -80.0 -80.0 5747.0 7265.0 0.0 7265.0 7265.0 7265.0 7265.0 7265.0 5747.0 1814.084218 1156.099867 229.0 740.00 1444.0 2846.50 5441.0 5747.0 2634.590820 ... 0.0 0.0 5747.0 0.027
RRRM2_BRN_GC_ISS-T_YNG_GY7 -80.0 -80.0 -80.0 5265.0 5776.0 0.0 5776.0 5776.0 5776.0 5776.0 5776.0 5265.0 2760.520608 1430.739583 714.0 1265.00 2990.0 3947.00 6339.0 5265.0 3175.982910 ... 0.0 0.0 5265.0 0.027
RRRM2_BRN_FLT_ISS-T_OLD_FO14 -80.0 -80.0 -80.0 3864.0 4260.0 0.0 4260.0 4260.0 4260.0 4260.0 4260.0 3864.0 2855.340321 1495.520733 646.0 1258.00 3082.0 4091.00 6259.0 3864.0 3060.737061 ... 0.0 0.0 3864.0 0.027
RRRM2_BRN_GC_ISS-T_YNG_GY1 -80.0 -80.0 -80.0 3672.0 3968.0 0.0 3968.0 3968.0 3968.0 3968.0 3968.0 3672.0 3256.512255 1538.412658 886.0 1613.00 3539.5 4524.75 6742.0 3672.0 3294.314209 ... 0.0 0.0 3672.0 0.027
RRRM2_BRN_FLT_ISS-T_YNG_FY2 -80.0 -80.0 -80.0 3730.0 4125.0 0.0 4125.0 4125.0 4125.0 4125.0 4125.0 3730.0 3048.478284 1670.078189 678.0 1280.00 3367.0 4444.25 6825.0 3730.0 3182.537109 ... 0.0 0.0 3730.0 0.027
RRRM2_BRN_FLT_ISS-T_OLD_FO17 -80.0 -80.0 -80.0 1321.0 1493.0 0.0 1493.0 1493.0 1493.0 1493.0 1493.0 1321.0 1323.376230 889.224703 288.0 568.00 963.0 2069.00 3710.0 1321.0 2313.327881 ... 0.0 0.0 1321.0 0.027
RRRM2_BRN_GC_ISS-T_OLD_GO16 -80.0 -80.0 -80.0 18386.0 20000.0 0.0 20000.0 20000.0 20000.0 20000.0 20000.0 18386.0 996.987110 727.946972 222.0 485.25 719.0 1210.00 3853.0 18386.0 1968.417236 ... 0.0 0.0 18386.0 0.027
RRRM2_BRN_FLT_ISS-T_OLD_FO16 -80.0 -80.0 -80.0 4050.0 4863.0 0.0 4863.0 4863.0 4863.0 4863.0 4863.0 4050.0 3231.898025 1648.761929 733.0 1444.25 3502.0 4531.00 7088.0 4050.0 3264.440430 ... 0.0 0.0 4050.0 0.027
RRRM2_BRN_GC_ISS-T_YNG_GY2 -80.0 -80.0 -80.0 9651.0 10779.0 0.0 10779.0 10779.0 10779.0 10779.0 10779.0 9651.0 2324.572998 1335.997180 474.0 937.50 2433.0 3476.50 5920.0 9651.0 2870.699463 ... 0.0 0.0 9651.0 0.027
RRRM2_BRN_FLT_ISS-T_YNG_FY5 -80.0 -80.0 -80.0 3914.0 4714.0 0.0 4714.0 4714.0 4714.0 4714.0 4714.0 3914.0 3249.220235 1517.653371 750.0 1620.00 3660.0 4410.75 6690.0 3914.0 3328.487061 ... 0.0 0.0 3914.0 0.027
leiden_n_neighbors leiden_min_dist majority_voting_probabilities \
std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max
sample
RRRM2_BRN_GC_ISS-T_YNG_GY4 6.939354e-18 0.027 0.027 0.027 0.027 0.027 7547.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 7547.0 1.3 2.220593e-16 1.3 1.3 1.3 1.3 1.3 5835.0 0.731971 0.351949 3.984064e-08 0.516274 0.938839 0.994447 1.0
RRRM2_BRN_GC_ISS-T_YNG_GY9 0.000000e+00 0.027 0.027 0.027 0.027 0.027 5465.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 5465.0 1.3 2.220649e-16 1.3 1.3 1.3 1.3 1.3 4925.0 0.806628 0.327564 8.496158e-09 0.812519 0.983840 0.998867 1.0
RRRM2_BRN_GC_ISS-T_OLD_GO18 6.939488e-18 0.027 0.027 0.027 0.027 0.027 5842.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 5842.0 1.3 2.220636e-16 1.3 1.3 1.3 1.3 1.3 5473.0 0.758686 0.337174 9.971479e-09 0.596715 0.955063 0.994763 1.0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 0.000000e+00 0.027 0.027 0.027 0.027 0.027 4922.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 4922.0 1.3 0.000000e+00 1.3 1.3 1.3 1.3 1.3 4701.0 0.808096 0.318233 1.434228e-07 0.781311 0.982027 0.998164 1.0
RRRM2_BRN_GC_ISS-T_OLD_GO19 6.939354e-18 0.027 0.027 0.027 0.027 0.027 7539.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 7539.0 1.3 2.220593e-16 1.3 1.3 1.3 1.3 1.3 6966.0 0.820181 0.316532 1.130676e-08 0.843462 0.985896 0.998742 1.0
RRRM2_BRN_GC_ISS-T_OLD_GO13 6.939859e-18 0.027 0.027 0.027 0.027 0.027 3595.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 3595.0 1.3 2.220755e-16 1.3 1.3 1.3 1.3 1.3 3162.0 0.853210 0.302856 9.360477e-11 0.928778 0.996575 0.999885 1.0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 0.000000e+00 0.027 0.027 0.027 0.027 0.027 5095.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 5095.0 1.3 2.220664e-16 1.3 1.3 1.3 1.3 1.3 4293.0 0.803511 0.333032 3.633419e-06 0.799202 0.988764 0.999306 1.0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 1.387968e-17 0.027 0.027 0.027 0.027 0.027 3669.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 3669.0 1.3 2.220749e-16 1.3 1.3 1.3 1.3 1.3 3285.0 0.732524 0.345319 3.307532e-05 0.515515 0.934969 0.993638 1.0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 6.939498e-18 0.027 0.027 0.027 0.027 0.027 5747.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 5747.0 1.3 4.441279e-16 1.3 1.3 1.3 1.3 1.3 3965.0 0.800322 0.329155 8.450188e-09 0.779898 0.979764 0.997917 1.0
RRRM2_BRN_GC_ISS-T_YNG_GY7 6.939553e-18 0.027 0.027 0.027 0.027 0.027 5265.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 5265.0 1.3 2.220657e-16 1.3 1.3 1.3 1.3 1.3 4612.0 0.844319 0.301817 4.859075e-09 0.900802 0.993400 0.999571 1.0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 6.939792e-18 0.027 0.027 0.027 0.027 0.027 3864.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 3864.0 1.3 2.220733e-16 1.3 1.3 1.3 1.3 1.3 3635.0 0.834795 0.306314 1.359645e-11 0.878763 0.990592 0.999394 1.0
RRRM2_BRN_GC_ISS-T_YNG_GY1 1.040976e-17 0.027 0.027 0.027 0.027 0.027 3672.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 3672.0 1.3 0.000000e+00 1.3 1.3 1.3 1.3 1.3 3283.0 0.818317 0.319496 1.352452e-08 0.840154 0.989572 0.999468 1.0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 1.040974e-17 0.027 0.027 0.027 0.027 0.027 3730.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 3730.0 1.3 0.000000e+00 1.3 1.3 1.3 1.3 1.3 3402.0 0.818877 0.325189 1.072093e-07 0.842627 0.989798 0.999319 1.0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 0.000000e+00 0.027 0.027 0.027 0.027 0.027 1321.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 1321.0 1.3 2.221287e-16 1.3 1.3 1.3 1.3 1.3 615.0 0.759735 0.344848 1.527736e-05 0.634516 0.963477 0.996633 1.0
RRRM2_BRN_GC_ISS-T_OLD_GO16 1.040862e-17 0.027 0.027 0.027 0.027 0.027 18386.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 18386.0 1.3 2.220506e-16 1.3 1.3 1.3 1.3 1.3 5851.0 0.731904 0.348553 6.960821e-07 0.502938 0.938182 0.993427 1.0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 6.939751e-18 0.027 0.027 0.027 0.027 0.027 4050.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 4050.0 1.3 2.220720e-16 1.3 1.3 1.3 1.3 1.3 3678.0 0.816042 0.326740 7.075571e-06 0.847619 0.988311 0.999079 1.0
RRRM2_BRN_GC_ISS-T_YNG_GY2 1.040888e-17 0.027 0.027 0.027 0.027 0.027 9651.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 9651.0 1.3 2.220561e-16 1.3 1.3 1.3 1.3 1.3 8842.0 0.810352 0.312684 3.324217e-09 0.787407 0.980535 0.998094 1.0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 1.040967e-17 0.027 0.027 0.027 0.027 0.027 3914.0 40.0 0.0 40.0 40.0 40.0 40.0 40.0 3914.0 1.3 2.220730e-16 1.3 1.3 1.3 1.3 1.3 3714.0 0.844041 0.290406 1.649057e-06 0.878605 0.988782 0.998847 1.0
Spaceflight Aged
count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max
sample
RRRM2_BRN_GC_ISS-T_YNG_GY4 7547.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7547.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_GC_ISS-T_YNG_GY9 5465.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5465.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_GC_ISS-T_OLD_GO18 5842.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5842.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 4922.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 4922.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_GC_ISS-T_OLD_GO19 7539.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7539.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_GC_ISS-T_OLD_GO13 3595.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3595.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 5095.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 5095.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 3669.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 3669.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 5747.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 5747.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_GC_ISS-T_YNG_GY7 5265.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5265.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 3864.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 3864.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_GC_ISS-T_YNG_GY1 3672.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3672.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 3730.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 3730.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 1321.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 1321.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_GC_ISS-T_OLD_GO16 18386.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 18386.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 4050.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 4050.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_GC_ISS-T_YNG_GY2 9651.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 9651.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 3914.0 1.0 0.0 1.0 1.0 1.0 1.0 1.0 3914.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
[18 rows x 248 columns]
Out[ ]:
| Group | sample | Characteristics[Organism] | Term Source REF | Term Accession Number | Characteristics[Strain] | Term Source REF.1 | Term Accession Number.1 | Characteristics[Genotype] | Term Source REF.2 | Term Accession Number.2 | Characteristics[Animal Source] | Characteristics[Sex] | Term Source REF.3 | Term Accession Number.3 | Factor Value[Spaceflight] | Term Source REF.4 | Term Accession Number.4 | Factor Value[Age] | Unit | Term Source REF.5 | Term Accession Number.5 | Characteristics[Material Type] | Term Source REF.6 | Term Accession Number.6 | Characteristics[diet] | Characteristics[Feeding Schedule] | Characteristics[Age at Euthanasia] | Unit.1 | Term Source REF.7 | Term Accession Number.7 | Protocol REF | Parameter Value[habitat] | Parameter Value[Enrichment material] | Parameter Value[duration] | Unit.2 | Term Source REF.8 | Term Accession Number.8 | Parameter Value[light cycle] | Protocol REF.1 | Parameter Value[Euthanasia Method] | Parameter Value[Sample Preservation Method] | Term Source REF.9 | Term Accession Number.9 | Parameter Value[Sample Storage Temperature] | Unit.3 | Term Source REF.10 | Term Accession Number.10 | Comment[RFID] | Comment[Euthanasia Date] | ... | min_max_genes | min_max_cells | max_mt | min_max_counts | zero_center | max_value | doublet_detection | n_genes_by_counts | total_counts | log1p_n_genes_by_counts | log1p_total_counts | total_counts_mt | pct_counts_mt | log1p_total_counts_mt | total_counts_ribo | pct_counts_ribo | log1p_total_counts_ribo | total_counts_hb | pct_counts_hb | log1p_total_counts_hb | n_counts | n_genes | doublet_score | predicted_doublet | resolution_individual | min_dist_individual | leiden_individual | annotation_by_markers_individual | annotation_by_markers_individual_heterogeneous_collapsed | _scvi_batch | _scvi_labels | annotation_scanvi | kws_integrate | leiden | leiden_resolution | leiden_n_neighbors | leiden_min_dist | annotation_by_overlap | annotation_toppgene | predicted_labels | majority_voting | majority_voting_probabilities | majority_voting_short | annotation_majority_voting | Age_Start | Age_End | Condition | Spaceflight | Aged | annotation_by_overlap_hierarchy | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AAACAGCCAATCGCAC-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 | Ground Control | 12 Weeks | RRRM2_BRN_GC_ISS-T_YNG_GY4 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Ground Control | OSD | https://osdr.nasa.gov/ | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E2A671967 | 18-Sep-2019 | ... | [321.0, None] | [20, None] | 10 | [414.0, 11122.175000000005] | True | 10 | drop | 2074 | 2960.899414 | 7.637716 | 7.993586 | 13.329097 | 0.450171 | 2.662292 | 10.466295 | 0.353484 | 2.439412 | 1.618296 | 0.054656 | 0.962524 | 4962.0 | 2152 | 0.030852 | False | 0.15 | 1.5 | 2 | Excitatory | Excitatory | 0 | 0 | Excitatory | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 2 | 0.027 | 40 | 1.3 | Excitatory | Excitatory | 006 L4/5 IT CTX Glut | 006 L4/5 IT CTX Glut | 0.834218 | L4/5 IT CTX Glut | Glutamatergic | 12 Weeks | 20 Weeks | Ground Control | 0 | 0 | Neuron |
| AAACAGCCAGCACCAT-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 | Ground Control | 12 Weeks | RRRM2_BRN_GC_ISS-T_YNG_GY4 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Ground Control | OSD | https://osdr.nasa.gov/ | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E2A671967 | 18-Sep-2019 | ... | [321.0, None] | [20, None] | 10 | [414.0, 11122.175000000005] | True | 10 | drop | 1568 | 2710.792725 | 7.358194 | 7.905365 | 13.841085 | 0.510592 | 2.697399 | 13.840614 | 0.510574 | 2.697368 | 1.474337 | 0.054388 | 0.905972 | 2994.0 | 1636 | 0.052691 | False | 0.15 | 1.5 | 12 | Excitatory | Excitatory | 0 | 0 | Excitatory | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 10 | 0.027 | 40 | 1.3 | Excitatory-Inhibitory | Excitatory | 149 PVT-PT Ntrk1 Glut | 150 CM-IAD-CL-PCN Sema5b Glut | 0.000921 | CM-IAD-CL-PCN Sema5b Glut | Glutamatergic | 12 Weeks | 20 Weeks | Ground Control | 0 | 0 | Neuron |
| AAACAGCCAGGTTCAC-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 | Ground Control | 12 Weeks | RRRM2_BRN_GC_ISS-T_YNG_GY4 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Ground Control | OSD | https://osdr.nasa.gov/ | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E2A671967 | 18-Sep-2019 | ... | [321.0, None] | [20, None] | 10 | [414.0, 11122.175000000005] | True | 10 | drop | 2953 | 3351.134033 | 7.990915 | 8.117352 | 1.542924 | 0.046042 | 0.933315 | 8.764180 | 0.261529 | 2.278721 | 0.000000 | 0.000000 | 0.000000 | 8625.0 | 3080 | 0.014899 | False | 0.15 | 1.5 | 7 | Excitatory | Excitatory | 0 | 0 | Excitatory | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 7 | 0.027 | 40 | 1.3 | Excitatory | Excitatory | 029 L6b CTX Glut | 029 L6b CTX Glut | 0.331972 | L6b CTX Glut | Glutamatergic | 12 Weeks | 20 Weeks | Ground Control | 0 | 0 | Neuron |
| AAACAGCCATCAATCG-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 | Ground Control | 12 Weeks | RRRM2_BRN_GC_ISS-T_YNG_GY4 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Ground Control | OSD | https://osdr.nasa.gov/ | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E2A671967 | 18-Sep-2019 | ... | [321.0, None] | [20, None] | 10 | [414.0, 11122.175000000005] | True | 10 | drop | 1071 | 2206.367188 | 6.977281 | 7.699556 | 5.162194 | 0.233968 | 1.818433 | 8.940096 | 0.405195 | 2.296577 | 0.000000 | 0.000000 | 0.000000 | 2017.0 | 1116 | 0.018405 | False | 0.15 | 1.5 | 8 | Oligodendrocyte | Oligodendrocyte | 0 | 0 | Oligodendrocyte | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 5 | 0.027 | 40 | 1.3 | Oligodendrocyte | Oligodendrocyte | 327 Oligo NN | 327 Oligo NN | 0.999615 | Oligo NN | Oligodendrocyte | 12 Weeks | 20 Weeks | Ground Control | 0 | 0 | Macroglia |
| AAACAGCCATGTCAAT-1_RRRM2_BRN_GC_ISS-T_YNG_GY4 | Ground Control | 12 Weeks | RRRM2_BRN_GC_ISS-T_YNG_GY4 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Ground Control | OSD | https://osdr.nasa.gov/ | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Liquid Nitrogen | NCIT | http://purl.obolibrary.org/obo/NCIT_C68796 | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E2A671967 | 18-Sep-2019 | ... | [321.0, None] | [20, None] | 10 | [414.0, 11122.175000000005] | True | 10 | drop | 2527 | 3219.782959 | 7.835184 | 8.077380 | 7.173846 | 0.222805 | 2.100940 | 9.022173 | 0.280211 | 2.304800 | 0.000000 | 0.000000 | 0.000000 | 6688.0 | 2614 | 0.004741 | False | 0.15 | 1.5 | 7 | Excitatory | Excitatory | 0 | 0 | Excitatory | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 7 | 0.027 | 40 | 1.3 | Excitatory | Excitatory | 030 L6 CT CTX Glut | 030 L6 CT CTX Glut | 0.995431 | L6 CT CTX Glut | Glutamatergic | 12 Weeks | 20 Weeks | Ground Control | 0 | 0 | Neuron |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| TTTGTGTTCCCTGATC-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Space Flight | 12 Weeks | RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Space Flight | MESH | http://purl.bioontology.org/ontology/MESH/D013026 | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Cryochiller | OSD | https://osdr.nasa.gov/ | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3E102A12 | 16-Sep-2019 | ... | [809.825, None] | [20, None] | 10 | [1300.3, 32231.825000000015] | True | 10 | drop | 3951 | 3698.035645 | 8.281977 | 8.215827 | 15.195225 | 0.410900 | 2.784716 | 32.415512 | 0.876560 | 3.509020 | 0.000000 | 0.000000 | 0.000000 | 12819.0 | 4151 | 0.006702 | False | 0.15 | 1.5 | 5 | Excitatory-Inhibitory | Excitatory-Inhibitory | 17 | 0 | Excitatory-Inhibitory | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 7 | 0.027 | 40 | 1.3 | Excitatory | Excitatory | 030 L6 CT CTX Glut | 030 L6 CT CTX Glut | 0.651805 | L6 CT CTX Glut | Glutamatergic | 12 Weeks | 20 Weeks | Space Flight | 1 | 0 | Neuron |
| TTTGTGTTCCGTAAAC-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Space Flight | 12 Weeks | RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Space Flight | MESH | http://purl.bioontology.org/ontology/MESH/D013026 | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Cryochiller | OSD | https://osdr.nasa.gov/ | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3E102A12 | 16-Sep-2019 | ... | [809.825, None] | [20, None] | 10 | [1300.3, 32231.825000000015] | True | 10 | drop | 4371 | 3844.071045 | 8.382976 | 8.254547 | 10.088642 | 0.262447 | 2.405921 | 46.482113 | 1.209190 | 3.860353 | 0.498700 | 0.012973 | 0.404598 | 15502.0 | 4645 | 0.007560 | False | 0.15 | 1.5 | 3 | Excitatory | Excitatory | 17 | 0 | Excitatory | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 2 | 0.027 | 40 | 1.3 | Excitatory | Excitatory | 016 CA1-ProS Glut | 017 CA3 Glut | 0.023398 | CA3 Glut | Glutamatergic | 12 Weeks | 20 Weeks | Space Flight | 1 | 0 | Neuron |
| TTTGTGTTCTGTGCAG-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Space Flight | 12 Weeks | RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Space Flight | MESH | http://purl.bioontology.org/ontology/MESH/D013026 | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Cryochiller | OSD | https://osdr.nasa.gov/ | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3E102A12 | 16-Sep-2019 | ... | [809.825, None] | [20, None] | 10 | [1300.3, 32231.825000000015] | True | 10 | drop | 5541 | 3862.635010 | 8.620111 | 8.259364 | 17.424606 | 0.451107 | 2.913687 | 27.956829 | 0.723776 | 3.365806 | 0.000000 | 0.000000 | 0.000000 | 27623.0 | 5886 | 0.035294 | False | 0.15 | 1.5 | 1 | Excitatory | Excitatory | 17 | 0 | Excitatory | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 2 | 0.027 | 40 | 1.3 | Excitatory | Excitatory | 006 L4/5 IT CTX Glut | 006 L4/5 IT CTX Glut | 0.823794 | L4/5 IT CTX Glut | Glutamatergic | 12 Weeks | 20 Weeks | Space Flight | 1 | 0 | Neuron |
| TTTGTTGGTGTTAGCA-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Space Flight | 12 Weeks | RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Space Flight | MESH | http://purl.bioontology.org/ontology/MESH/D013026 | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Cryochiller | OSD | https://osdr.nasa.gov/ | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3E102A12 | 16-Sep-2019 | ... | [809.825, None] | [20, None] | 10 | [1300.3, 32231.825000000015] | True | 10 | drop | 845 | 1923.521484 | 6.740519 | 7.562433 | 24.807867 | 1.289711 | 3.250679 | 28.636005 | 1.488728 | 3.388990 | 0.000000 | 0.000000 | 0.000000 | 1469.0 | 873 | 0.007560 | False | 0.15 | 1.5 | 8 | Oligodendrocyte | Oligodendrocyte | 17 | 0 | Oligodendrocyte | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 5 | 0.027 | 40 | 1.3 | Oligodendrocyte | Oligodendrocyte | 327 Oligo NN | 327 Oligo NN | 0.993974 | Oligo NN | Oligodendrocyte | 12 Weeks | 20 Weeks | Space Flight | 1 | 0 | Macroglia |
| TTTGTTGGTTAAGTGT-1_RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Space Flight | 12 Weeks | RRRM2_BRN_FLT_ISS-T_YNG_FY5 | Mus musculus | NCBITAXON | http://purl.bioontology.org/ontology/NCBITAXON/10090 | C57BL/6NTac | EFO | http://www.ebi.ac.uk/efo/EFO_0020093 | Wild Type | NCIT | http://purl.obolibrary.org/obo/NCIT_C62195 | Taconic Biosciences | Female | MESH | http://purl.bioontology.org/ontology/MESH/D005260 | Space Flight | MESH | http://purl.bioontology.org/ontology/MESH/D013026 | 12 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Left cerebral hemisphere | FMA | http://purl.org/sig/ont/fma/fma61819 | Nutrient Upgraded Rodent Food Bar (NuRFB) | ad libitum | 20 | week | UO | http://purl.obolibrary.org/obo/UO_0000034 | Animal Husbandry | Rodent Flight Hardware (Transporter and Habitat) | Hut | 53 | day | UO | http://purl.obolibrary.org/obo/UO_0000033 | 12 h light/dark cycle | sample collection | Bilateral thoracotomy with sedation, Cardiac puncture with sedation, Ketamine/xylazine injection | Cryochiller | OSD | https://osdr.nasa.gov/ | -80 | degree Celsius | UO | http://purl.obolibrary.org/obo/UO_0000027 | 6E3E102A12 | 16-Sep-2019 | ... | [809.825, None] | [20, None] | 10 | [1300.3, 32231.825000000015] | True | 10 | drop | 3268 | 3583.923096 | 8.092239 | 8.184492 | 7.856516 | 0.219216 | 2.181154 | 28.247156 | 0.788163 | 3.375782 | 1.151552 | 0.032131 | 0.766189 | 9258.0 | 3411 | 0.058924 | False | 0.15 | 1.5 | 1 | Excitatory | Excitatory | 17 | 0 | Excitatory | {'col_celltype': 'annotation_by_markers_individual_heterogeneous_collapsed', 'flavor': 'scanvi',... | 2 | 0.027 | 40 | 1.3 | Excitatory | Excitatory | 006 L4/5 IT CTX Glut | 006 L4/5 IT CTX Glut | 0.961207 | L4/5 IT CTX Glut | Glutamatergic | 12 Weeks | 20 Weeks | Space Flight | 1 | 0 | Neuron |
103274 rows × 101 columns
Data Descriptives¶
In [37]:
%matplotlib inline
# Detect Metadata Columns in AnnData Object
metadata_cols = self.rna.obs.groupby(col_sample).apply(
lambda x: list(x.columns[np.where(
x.apply(lambda y: len(y.unique()) == 1))[0]]))
metadata_cols = [x for x in metadata_cols.explode().unique() if all((
x in metadata_cols.loc[i] for i in metadata_cols.index.values))]
# Value Counts for Categorical Variables
metadata_cols_category = self.rna.obs[metadata_cols].dtypes[((
self.rna.obs[metadata_cols].dtypes == "category") | (self.rna.obs[
metadata_cols].apply(lambda x: len(x.unique()) < 6))) & (self.rna.obs[
metadata_cols].apply(lambda x: len(x.unique()) > 1))].index
for x in metadata_cols_category:
print(x, "\n\n", self.rna.obs[x].value_counts(), "\n\n")
if col_batch is not None: # by batch
if x != col_batch:
print(x, "\n\n", self.rna.obs[[x, col_batch]].groupby(
col_batch).value_counts(), "\n\n")
# Descriptives for Continuous Variables
print(self.rna.obs[self.rna.obs.columns.difference(
metadata_cols_category)].describe())
if col_batch is not None: # by batch
print(self.rna.obs[list(self.rna.obs.columns.difference(
metadata_cols_category)) + [col_batch]].groupby(col_batch).describe())
# Cells per Cluster
perc_clusters = round(100 * self.rna.obs.groupby(col_sample).value_counts(
[col_celltype], normalize=True).unstack(1), 2)
print(f"\n\n*** Cell Composition (%) ***\n\n{perc_clusters}")
# Number of Cells
n_cells = self.rna.obs.groupby(col_sample).apply(
lambda x: x.n_cells_original_sample.unique()[0],
include_groups=False).to_frame("Original Cell N").join(
self.rna.obs.groupby(col_sample).apply(
lambda x: x.shape[0], include_groups=False).to_frame("N Cells"))
n_cells.loc[:, "Percent_Filtered"] = round(100 * (1 - n_cells[
"N Cells"] / n_cells["Original Cell N"]), 2)
print("\n\n", n_cells.sort_values("Percent_Filtered"))
/tmp/ipykernel_33560/1416146960.py:4: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. metadata_cols = self.rna.obs.groupby(col_sample).apply(
Group
Group
Ground Control | 29 Weeks 35362
Ground Control | 12 Weeks 31600
Space Flight | 29 Weeks 19904
Space Flight | 12 Weeks 16408
Name: count, dtype: int64
sample
sample
RRRM2_BRN_GC_ISS-T_OLD_GO16 18386
RRRM2_BRN_GC_ISS-T_YNG_GY2 9651
RRRM2_BRN_GC_ISS-T_YNG_GY4 7547
RRRM2_BRN_GC_ISS-T_OLD_GO19 7539
RRRM2_BRN_GC_ISS-T_OLD_GO18 5842
RRRM2_BRN_FLT_ISS-T_OLD_FO19 5747
RRRM2_BRN_GC_ISS-T_YNG_GY9 5465
RRRM2_BRN_GC_ISS-T_YNG_GY7 5265
RRRM2_BRN_FLT_ISS-T_YNG_FY8 5095
RRRM2_BRN_FLT_ISS-T_OLD_FO20 4922
RRRM2_BRN_FLT_ISS-T_OLD_FO16 4050
RRRM2_BRN_FLT_ISS-T_YNG_FY5 3914
RRRM2_BRN_FLT_ISS-T_OLD_FO14 3864
RRRM2_BRN_FLT_ISS-T_YNG_FY2 3730
RRRM2_BRN_GC_ISS-T_YNG_GY1 3672
RRRM2_BRN_FLT_ISS-T_YNG_FY7 3669
RRRM2_BRN_GC_ISS-T_OLD_GO13 3595
RRRM2_BRN_FLT_ISS-T_OLD_FO17 1321
Name: count, dtype: int64
sample
Group sample
Ground Control | 12 Weeks RRRM2_BRN_GC_ISS-T_YNG_GY2 9651
RRRM2_BRN_GC_ISS-T_YNG_GY4 7547
RRRM2_BRN_GC_ISS-T_YNG_GY9 5465
RRRM2_BRN_GC_ISS-T_YNG_GY7 5265
RRRM2_BRN_GC_ISS-T_YNG_GY1 3672
RRRM2_BRN_GC_ISS-T_OLD_GO18 0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 0
RRRM2_BRN_GC_ISS-T_OLD_GO19 0
RRRM2_BRN_GC_ISS-T_OLD_GO13 0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 0
RRRM2_BRN_GC_ISS-T_OLD_GO16 0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 0
Ground Control | 29 Weeks RRRM2_BRN_GC_ISS-T_OLD_GO16 18386
RRRM2_BRN_GC_ISS-T_OLD_GO19 7539
RRRM2_BRN_GC_ISS-T_OLD_GO18 5842
RRRM2_BRN_GC_ISS-T_OLD_GO13 3595
RRRM2_BRN_GC_ISS-T_YNG_GY4 0
RRRM2_BRN_GC_ISS-T_YNG_GY9 0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 0
RRRM2_BRN_GC_ISS-T_YNG_GY7 0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 0
RRRM2_BRN_GC_ISS-T_YNG_GY1 0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 0
RRRM2_BRN_GC_ISS-T_YNG_GY2 0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 0
Space Flight | 12 Weeks RRRM2_BRN_FLT_ISS-T_YNG_FY8 5095
RRRM2_BRN_FLT_ISS-T_YNG_FY5 3914
RRRM2_BRN_FLT_ISS-T_YNG_FY2 3730
RRRM2_BRN_FLT_ISS-T_YNG_FY7 3669
RRRM2_BRN_GC_ISS-T_YNG_GY4 0
RRRM2_BRN_GC_ISS-T_YNG_GY9 0
RRRM2_BRN_GC_ISS-T_OLD_GO18 0
RRRM2_BRN_FLT_ISS-T_OLD_FO20 0
RRRM2_BRN_GC_ISS-T_OLD_GO19 0
RRRM2_BRN_GC_ISS-T_OLD_GO13 0
RRRM2_BRN_FLT_ISS-T_OLD_FO19 0
RRRM2_BRN_GC_ISS-T_YNG_GY7 0
RRRM2_BRN_FLT_ISS-T_OLD_FO14 0
RRRM2_BRN_GC_ISS-T_YNG_GY1 0
RRRM2_BRN_FLT_ISS-T_OLD_FO17 0
RRRM2_BRN_GC_ISS-T_OLD_GO16 0
RRRM2_BRN_FLT_ISS-T_OLD_FO16 0
RRRM2_BRN_GC_ISS-T_YNG_GY2 0
Space Flight | 29 Weeks RRRM2_BRN_FLT_ISS-T_OLD_FO19 5747
RRRM2_BRN_FLT_ISS-T_OLD_FO20 4922
RRRM2_BRN_FLT_ISS-T_OLD_FO16 4050
RRRM2_BRN_FLT_ISS-T_OLD_FO14 3864
RRRM2_BRN_FLT_ISS-T_OLD_FO17 1321
RRRM2_BRN_GC_ISS-T_YNG_GY4 0
RRRM2_BRN_GC_ISS-T_YNG_GY9 0
RRRM2_BRN_GC_ISS-T_OLD_GO18 0
RRRM2_BRN_GC_ISS-T_OLD_GO19 0
RRRM2_BRN_GC_ISS-T_OLD_GO13 0
RRRM2_BRN_FLT_ISS-T_YNG_FY8 0
RRRM2_BRN_FLT_ISS-T_YNG_FY7 0
RRRM2_BRN_GC_ISS-T_YNG_GY7 0
RRRM2_BRN_GC_ISS-T_YNG_GY1 0
RRRM2_BRN_FLT_ISS-T_YNG_FY2 0
RRRM2_BRN_GC_ISS-T_OLD_GO16 0
RRRM2_BRN_GC_ISS-T_YNG_GY2 0
RRRM2_BRN_FLT_ISS-T_YNG_FY5 0
Name: count, dtype: int64
Factor Value[Spaceflight]
Factor Value[Spaceflight]
Ground Control 66962
Space Flight 36312
Name: count, dtype: int64
Factor Value[Spaceflight]
Group Factor Value[Spaceflight]
Ground Control | 12 Weeks Ground Control 31600
Space Flight 0
Ground Control | 29 Weeks Ground Control 35362
Space Flight 0
Space Flight | 12 Weeks Space Flight 16408
Ground Control 0
Space Flight | 29 Weeks Space Flight 19904
Ground Control 0
Name: count, dtype: int64
Term Source REF.4
Term Source REF.4
OSD 66962
MESH 36312
Name: count, dtype: int64
Term Source REF.4
Group Term Source REF.4
Ground Control | 12 Weeks OSD 31600
MESH 0
Ground Control | 29 Weeks OSD 35362
MESH 0
Space Flight | 12 Weeks MESH 16408
OSD 0
Space Flight | 29 Weeks MESH 19904
OSD 0
Name: count, dtype: int64
Term Accession Number.4
Term Accession Number.4
https://osdr.nasa.gov/ 66962
http://purl.bioontology.org/ontology/MESH/D013026 36312
Name: count, dtype: int64
Term Accession Number.4
Group Term Accession Number.4
Ground Control | 12 Weeks https://osdr.nasa.gov/ 31600
http://purl.bioontology.org/ontology/MESH/D013026 0
Ground Control | 29 Weeks https://osdr.nasa.gov/ 35362
http://purl.bioontology.org/ontology/MESH/D013026 0
Space Flight | 12 Weeks http://purl.bioontology.org/ontology/MESH/D013026 16408
https://osdr.nasa.gov/ 0
Space Flight | 29 Weeks http://purl.bioontology.org/ontology/MESH/D013026 19904
https://osdr.nasa.gov/ 0
Name: count, dtype: int64
Factor Value[Age]
Factor Value[Age]
29 55266
12 48008
Name: count, dtype: int64
Factor Value[Age]
Group Factor Value[Age]
Ground Control | 12 Weeks 12 31600
29 0
Ground Control | 29 Weeks 29 35362
12 0
Space Flight | 12 Weeks 12 16408
29 0
Space Flight | 29 Weeks 29 19904
12 0
Name: count, dtype: int64
Characteristics[Age at Euthanasia]
Characteristics[Age at Euthanasia]
37 55266
20 48008
Name: count, dtype: int64
Characteristics[Age at Euthanasia]
Group Characteristics[Age at Euthanasia]
Ground Control | 12 Weeks 20 31600
37 0
Ground Control | 29 Weeks 37 35362
20 0
Space Flight | 12 Weeks 20 16408
37 0
Space Flight | 29 Weeks 37 19904
20 0
Name: count, dtype: int64
Parameter Value[Sample Preservation Method]
Parameter Value[Sample Preservation Method]
Liquid Nitrogen 66962
Cryochiller 36312
Name: count, dtype: int64
Parameter Value[Sample Preservation Method]
Group Parameter Value[Sample Preservation Method]
Ground Control | 12 Weeks Liquid Nitrogen 31600
Cryochiller 0
Ground Control | 29 Weeks Liquid Nitrogen 35362
Cryochiller 0
Space Flight | 12 Weeks Cryochiller 16408
Liquid Nitrogen 0
Space Flight | 29 Weeks Cryochiller 19904
Liquid Nitrogen 0
Name: count, dtype: int64
Term Source REF.9
Term Source REF.9
NCIT 66962
OSD 36312
Name: count, dtype: int64
Term Source REF.9
Group Term Source REF.9
Ground Control | 12 Weeks NCIT 31600
OSD 0
Ground Control | 29 Weeks NCIT 35362
OSD 0
Space Flight | 12 Weeks OSD 16408
NCIT 0
Space Flight | 29 Weeks OSD 19904
NCIT 0
Name: count, dtype: int64
Term Accession Number.9
Term Accession Number.9
http://purl.obolibrary.org/obo/NCIT_C68796 66962
https://osdr.nasa.gov/ 36312
Name: count, dtype: int64
Term Accession Number.9
Group Term Accession Number.9
Ground Control | 12 Weeks http://purl.obolibrary.org/obo/NCIT_C68796 31600
https://osdr.nasa.gov/ 0
Ground Control | 29 Weeks http://purl.obolibrary.org/obo/NCIT_C68796 35362
https://osdr.nasa.gov/ 0
Space Flight | 12 Weeks https://osdr.nasa.gov/ 16408
http://purl.obolibrary.org/obo/NCIT_C68796 0
Space Flight | 29 Weeks https://osdr.nasa.gov/ 19904
http://purl.obolibrary.org/obo/NCIT_C68796 0
Name: count, dtype: int64
Comment[RFID]
Comment[RFID]
6E35413D61 18386
6E371E2032 9651
6E2A671967 7547
6E3C342F47 7539
6E3C216122 5842
6E3D2C2D2C 5747
6E3C705065 5465
6E28536840 5265
6E3C42091B 5095
6E272D285A 4922
6E27313758 4050
6E3E102A12 3914
6E2A180C12 3864
6E394B6C23 3730
6E28307F36 3672
6E3E325E7C 3669
6E3A3C1239 3595
6E353B735A 1321
Name: count, dtype: int64
Comment[RFID]
Group Comment[RFID]
Ground Control | 12 Weeks 6E371E2032 9651
6E2A671967 7547
6E3C705065 5465
6E28536840 5265
6E28307F36 3672
6E2A180C12 0
6E3A3C1239 0
6E3C342F47 0
6E3C42091B 0
6E3C216122 0
6E3D2C2D2C 0
6E3E102A12 0
6E3E325E7C 0
6E272D285A 0
6E353B735A 0
6E394B6C23 0
6E35413D61 0
6E27313758 0
Ground Control | 29 Weeks 6E35413D61 18386
6E3C342F47 7539
6E3C216122 5842
6E3A3C1239 3595
6E2A180C12 0
6E2A671967 0
6E3C42091B 0
6E3C705065 0
6E3D2C2D2C 0
6E3E102A12 0
6E3E325E7C 0
6E272D285A 0
6E353B735A 0
6E371E2032 0
6E394B6C23 0
6E28307F36 0
6E27313758 0
6E28536840 0
Space Flight | 12 Weeks 6E3C42091B 5095
6E3E102A12 3914
6E394B6C23 3730
6E3E325E7C 3669
6E2A180C12 0
6E2A671967 0
6E3A3C1239 0
6E3C342F47 0
6E3C216122 0
6E3C705065 0
6E3D2C2D2C 0
6E272D285A 0
6E353B735A 0
6E371E2032 0
6E28307F36 0
6E35413D61 0
6E27313758 0
6E28536840 0
Space Flight | 29 Weeks 6E3D2C2D2C 5747
6E272D285A 4922
6E27313758 4050
6E2A180C12 3864
6E353B735A 1321
6E2A671967 0
6E3A3C1239 0
6E3C342F47 0
6E3C42091B 0
6E3C216122 0
6E3C705065 0
6E3E102A12 0
6E3E325E7C 0
6E371E2032 0
6E394B6C23 0
6E28307F36 0
6E35413D61 0
6E28536840 0
Name: count, dtype: int64
Comment[Euthanasia Date]
Comment[Euthanasia Date]
20-Sep-2019 31767
18-Sep-2019 29999
19-Sep-2019 25100
17-Sep-2019 8764
16-Sep-2019 7644
Name: count, dtype: int64
Comment[Euthanasia Date]
Group Comment[Euthanasia Date]
Ground Control | 12 Weeks 18-Sep-2019 26135
19-Sep-2019 5465
16-Sep-2019 0
17-Sep-2019 0
20-Sep-2019 0
Ground Control | 29 Weeks 20-Sep-2019 31767
19-Sep-2019 3595
16-Sep-2019 0
17-Sep-2019 0
18-Sep-2019 0
Space Flight | 12 Weeks 17-Sep-2019 8764
16-Sep-2019 7644
18-Sep-2019 0
19-Sep-2019 0
20-Sep-2019 0
Space Flight | 29 Weeks 19-Sep-2019 16040
18-Sep-2019 3864
16-Sep-2019 0
17-Sep-2019 0
20-Sep-2019 0
Name: count, dtype: int64
min_max_genes
min_max_genes
[201.0, None] 18386
[507.0, None] 9651
[321.0, None] 7547
[666.0, None] 7539
[326.1, None] 5842
[200, None] 5747
[703.0, None] 5465
[798.75, None] 5265
[481.425, None] 5095
[628.0, None] 4922
[766.0, None] 4050
[809.825, None] 3914
[719.475, None] 3864
[700.2, None] 3730
[923.4000000000001, None] 3672
[331.925, None] 3669
[822.975, None] 3595
[316.3, None] 1321
Name: count, dtype: int64
min_max_genes
Group min_max_genes
Ground Control | 12 Weeks [507.0, None] 9651
[321.0, None] 7547
[703.0, None] 5465
[798.75, None] 5265
[923.4000000000001, None] 3672
[200, None] 0
[201.0, None] 0
[316.3, None] 0
[326.1, None] 0
[331.925, None] 0
[481.425, None] 0
[628.0, None] 0
[666.0, None] 0
[700.2, None] 0
[719.475, None] 0
[766.0, None] 0
[809.825, None] 0
[822.975, None] 0
Ground Control | 29 Weeks [201.0, None] 18386
[666.0, None] 7539
[326.1, None] 5842
[822.975, None] 3595
[200, None] 0
[316.3, None] 0
[321.0, None] 0
[331.925, None] 0
[481.425, None] 0
[507.0, None] 0
[628.0, None] 0
[700.2, None] 0
[703.0, None] 0
[719.475, None] 0
[766.0, None] 0
[798.75, None] 0
[809.825, None] 0
[923.4000000000001, None] 0
Space Flight | 12 Weeks [481.425, None] 5095
[809.825, None] 3914
[700.2, None] 3730
[331.925, None] 3669
[200, None] 0
[201.0, None] 0
[316.3, None] 0
[321.0, None] 0
[326.1, None] 0
[507.0, None] 0
[628.0, None] 0
[666.0, None] 0
[703.0, None] 0
[719.475, None] 0
[766.0, None] 0
[798.75, None] 0
[822.975, None] 0
[923.4000000000001, None] 0
Space Flight | 29 Weeks [200, None] 5747
[628.0, None] 4922
[766.0, None] 4050
[719.475, None] 3864
[316.3, None] 1321
[201.0, None] 0
[321.0, None] 0
[326.1, None] 0
[331.925, None] 0
[481.425, None] 0
[507.0, None] 0
[666.0, None] 0
[700.2, None] 0
[703.0, None] 0
[798.75, None] 0
[809.825, None] 0
[822.975, None] 0
[923.4000000000001, None] 0
Name: count, dtype: int64
min_max_counts
min_max_counts
[300, 10018.199999999983] 18386
[696.45, 23662.899999999972] 9651
[414.0, 11122.175000000005] 7547
[942.0, 21190.0] 7539
[422.1, 13468.399999999998] 5842
[300, 14724.999999999998] 5747
[1006.1500000000001, 23784.40000000001] 5465
[1176.375, 28050.25] 5265
[681.7, 27907.149999999994] 5095
[895.0, 19711.0] 4922
[1213.2, 36130.25] 4050
[1300.3, 32231.825000000015] 3914
[1128.9, 33910.62499999997] 3864
[1087.2, 35338.0] 3730
[1489.7, 37456.77499999999] 3672
[452.77500000000003, 13453.3] 3669
[1282.925, 23156.550000000007] 3595
[376.3, 11766.900000000009] 1321
Name: count, dtype: int64
min_max_counts
Group min_max_counts
Ground Control | 12 Weeks [696.45, 23662.899999999972] 9651
[414.0, 11122.175000000005] 7547
[1006.1500000000001, 23784.40000000001] 5465
[1176.375, 28050.25] 5265
[1489.7, 37456.77499999999] 3672
[300, 10018.199999999983] 0
[300, 14724.999999999998] 0
[376.3, 11766.900000000009] 0
[422.1, 13468.399999999998] 0
[452.77500000000003, 13453.3] 0
[681.7, 27907.149999999994] 0
[895.0, 19711.0] 0
[942.0, 21190.0] 0
[1087.2, 35338.0] 0
[1128.9, 33910.62499999997] 0
[1213.2, 36130.25] 0
[1282.925, 23156.550000000007] 0
[1300.3, 32231.825000000015] 0
Ground Control | 29 Weeks [300, 10018.199999999983] 18386
[942.0, 21190.0] 7539
[422.1, 13468.399999999998] 5842
[1282.925, 23156.550000000007] 3595
[300, 14724.999999999998] 0
[376.3, 11766.900000000009] 0
[414.0, 11122.175000000005] 0
[452.77500000000003, 13453.3] 0
[681.7, 27907.149999999994] 0
[696.45, 23662.899999999972] 0
[895.0, 19711.0] 0
[1006.1500000000001, 23784.40000000001] 0
[1087.2, 35338.0] 0
[1128.9, 33910.62499999997] 0
[1176.375, 28050.25] 0
[1213.2, 36130.25] 0
[1300.3, 32231.825000000015] 0
[1489.7, 37456.77499999999] 0
Space Flight | 12 Weeks [681.7, 27907.149999999994] 5095
[1300.3, 32231.825000000015] 3914
[1087.2, 35338.0] 3730
[452.77500000000003, 13453.3] 3669
[300, 10018.199999999983] 0
[300, 14724.999999999998] 0
[376.3, 11766.900000000009] 0
[414.0, 11122.175000000005] 0
[422.1, 13468.399999999998] 0
[696.45, 23662.899999999972] 0
[895.0, 19711.0] 0
[942.0, 21190.0] 0
[1006.1500000000001, 23784.40000000001] 0
[1128.9, 33910.62499999997] 0
[1176.375, 28050.25] 0
[1213.2, 36130.25] 0
[1282.925, 23156.550000000007] 0
[1489.7, 37456.77499999999] 0
Space Flight | 29 Weeks [300, 14724.999999999998] 5747
[895.0, 19711.0] 4922
[1213.2, 36130.25] 4050
[1128.9, 33910.62499999997] 3864
[376.3, 11766.900000000009] 1321
[300, 10018.199999999983] 0
[414.0, 11122.175000000005] 0
[422.1, 13468.399999999998] 0
[452.77500000000003, 13453.3] 0
[681.7, 27907.149999999994] 0
[696.45, 23662.899999999972] 0
[942.0, 21190.0] 0
[1006.1500000000001, 23784.40000000001] 0
[1087.2, 35338.0] 0
[1176.375, 28050.25] 0
[1282.925, 23156.550000000007] 0
[1300.3, 32231.825000000015] 0
[1489.7, 37456.77499999999] 0
Name: count, dtype: int64
Age_Start
Age_Start
29 Weeks 55266
12 Weeks 48008
Name: count, dtype: int64
Age_Start
Group Age_Start
Ground Control | 12 Weeks 12 Weeks 31600
29 Weeks 0
Ground Control | 29 Weeks 29 Weeks 35362
12 Weeks 0
Space Flight | 12 Weeks 12 Weeks 16408
29 Weeks 0
Space Flight | 29 Weeks 29 Weeks 19904
12 Weeks 0
Name: count, dtype: int64
Age_End
Age_End
37 Weeks 55266
20 Weeks 48008
Name: count, dtype: int64
Age_End
Group Age_End
Ground Control | 12 Weeks 20 Weeks 31600
37 Weeks 0
Ground Control | 29 Weeks 37 Weeks 35362
20 Weeks 0
Space Flight | 12 Weeks 20 Weeks 16408
37 Weeks 0
Space Flight | 29 Weeks 37 Weeks 19904
20 Weeks 0
Name: count, dtype: int64
Condition
Condition
Ground Control 66962
Space Flight 36312
Name: count, dtype: int64
Condition
Group Condition
Ground Control | 12 Weeks Ground Control 31600
Space Flight 0
Ground Control | 29 Weeks Ground Control 35362
Space Flight 0
Space Flight | 12 Weeks Space Flight 16408
Ground Control 0
Space Flight | 29 Weeks Space Flight 19904
Ground Control 0
Name: count, dtype: int64
Spaceflight
Spaceflight
0 66962
1 36312
Name: count, dtype: int64
Spaceflight
Group Spaceflight
Ground Control | 12 Weeks 0 31600
1 0
Ground Control | 29 Weeks 0 35362
1 0
Space Flight | 12 Weeks 1 16408
0 0
Space Flight | 29 Weeks 1 19904
0 0
Name: count, dtype: int64
Aged Parameter Value[Sample Storage Temperature] Parameter Value[duration] Senescence_Threshold Senescence_Threshold_senmayo Senescence_Threshold_senmayo_3 _scvi_batch _scvi_labels doublet_score leiden_min_dist leiden_n_neighbors leiden_resolution log1p_n_genes_by_counts \
count 103274.0 103274.0 103274.0 103274.000000 103274.000000 103274.000000 103274.000000 103274.0 103274.000000 1.032740e+05 103274.0 1.032740e+05 103274.000000
mean 0.0 -80.0 53.0 0.133743 0.133743 0.095622 8.808926 0.0 0.041344 1.300000e+00 40.0 2.700000e-02 7.417736
std 0.0 0.0 0.0 0.042575 0.042575 0.034556 5.509775 0.0 0.044008 6.661370e-16 0.0 2.081678e-17 0.778077
min 0.0 -80.0 53.0 0.046570 0.046570 0.026312 0.000000 0.0 0.000939 1.300000e+00 40.0 2.700000e-02 5.407172
25% 0.0 -80.0 53.0 0.106507 0.106507 0.076821 4.000000 0.0 0.012658 1.300000e+00 40.0 2.700000e-02 6.786999
50% 0.0 -80.0 53.0 0.137236 0.137236 0.096417 9.000000 0.0 0.026517 1.300000e+00 40.0 2.700000e-02 7.514800
75% 0.0 -80.0 53.0 0.137236 0.137236 0.096417 14.000000 0.0 0.051769 1.300000e+00 40.0 2.700000e-02 8.114025
max 0.0 -80.0 53.0 0.243136 0.243136 0.186174 17.000000 0.0 0.427957 1.300000e+00 40.0 2.700000e-02 8.866300
log1p_total_counts log1p_total_counts_hb log1p_total_counts_mt log1p_total_counts_ribo majority_voting_probabilities min_dist_individual n_cells_original_sample n_counts n_genes n_genes_by_counts pct_counts_hb pct_counts_mt pct_counts_ribo resolution_individual \
count 103274.000000 103274.000000 103274.000000 103274.000000 8.023700e+04 103274.0 103274.000000 103274.000000 103274.000000 103274.000000 103274.000000 103274.000000 103274.000000 1.032740e+05
mean 7.870787 0.258264 2.375908 2.891556 7.987201e-01 1.5 8868.877617 6141.850098 2285.132850 2174.142524 0.014980 0.486775 0.829001 1.500000e-01
std 0.360491 0.392817 0.573531 0.849092 3.266262e-01 0.0 5555.444146 6050.134766 1536.856773 1450.048991 0.033168 0.374639 0.614566 5.551142e-17
min 6.675428 0.000000 0.000000 0.000000 1.359645e-11 1.5 1493.000000 300.000000 228.000000 222.000000 0.000000 0.000000 0.000000 1.500000e-01
25% 7.608359 0.000000 2.026199 2.401980 7.635981e-01 1.5 4863.000000 1469.000000 928.000000 885.250000 0.000000 0.217687 0.392694 1.500000e-01
50% 7.967166 0.000000 2.409908 2.958984 9.806495e-01 1.5 6503.000000 3866.000000 1918.000000 1834.000000 0.000000 0.390138 0.672651 1.500000e-01
75% 8.189143 0.555061 2.776883 3.533779 9.984541e-01 1.5 10779.000000 9198.000000 3499.000000 3340.000000 0.020060 0.641602 1.105970 1.500000e-01
max 8.447374 2.846624 3.873097 5.206285 1.000000e+00 1.5 20000.000000 37385.000000 7625.000000 7088.000000 0.965243 3.546953 8.065756 1.500000e-01
score_senmayo senscore total_counts total_counts_hb total_counts_mt total_counts_ribo
count 103274.000000 103274.000000 103274.000000 103274.000000 103274.000000 103274.000000
mean -0.021515 -0.021515 2778.911133 0.417356 11.512283 23.200752
std 0.068418 0.068418 884.448792 0.730279 6.752755 17.517857
min -0.264210 -0.264210 791.686646 0.000000 0.000000 0.000000
25% -0.068912 -0.068912 2013.969574 0.000000 6.585198 10.045021
50% -0.026115 -0.026115 2883.670288 0.000000 10.132935 18.278371
75% 0.019310 0.019310 3600.635742 0.742047 15.068856 33.253172
max 0.465130 0.465130 4661.812988 16.229515 47.091106 181.415146
Aged Parameter Value[Sample Storage Temperature] Parameter Value[duration] Senescence_Threshold \
count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50%
Group
Ground Control | 12 Weeks 31600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 31600.0 -80.0 0.0 -80.0 -80.0 -80.0 -80.0 -80.0 31600.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 31600.0 0.133375 0.047414 0.04657 0.106507 0.106507
Ground Control | 29 Weeks 35362.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 35362.0 -80.0 0.0 -80.0 -80.0 -80.0 -80.0 -80.0 35362.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 35362.0 0.134809 0.036736 0.04657 0.106507 0.137236
Space Flight | 12 Weeks 16408.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 16408.0 -80.0 0.0 -80.0 -80.0 -80.0 -80.0 -80.0 16408.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 16408.0 0.132107 0.045722 0.04657 0.104362 0.106507
Space Flight | 29 Weeks 19904.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 19904.0 -80.0 0.0 -80.0 -80.0 -80.0 -80.0 -80.0 19904.0 53.0 0.0 53.0 53.0 53.0 53.0 53.0 19904.0 0.133780 0.041360 0.04657 0.106507 0.137236
Senescence_Threshold_senmayo Senescence_Threshold_senmayo_3 _scvi_batch ... resolution_individual \
75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean ... 75%
Group ...
Ground Control | 12 Weeks 0.137236 0.243136 31600.0 0.133375 0.047414 0.04657 0.106507 0.106507 0.137236 0.243136 31600.0 0.095616 0.038344 0.026312 0.076821 0.076821 0.096417 0.186174 31600.0 7.837278 ... 0.15
Ground Control | 29 Weeks 0.137236 0.243136 35362.0 0.134809 0.036736 0.04657 0.106507 0.137236 0.137236 0.243136 35362.0 0.096102 0.029704 0.026312 0.076821 0.096417 0.096417 0.186174 35362.0 8.970618 ... 0.15
Space Flight | 12 Weeks 0.137236 0.243136 16408.0 0.132107 0.045722 0.04657 0.104362 0.106507 0.137236 0.243136 16408.0 0.094648 0.037508 0.026312 0.070340 0.076821 0.096417 0.186174 16408.0 10.211543 ... 0.15
Space Flight | 29 Weeks 0.137236 0.243136 19904.0 0.133780 0.041360 0.04657 0.106507 0.137236 0.137236 0.243136 19904.0 0.095584 0.033665 0.026312 0.076821 0.096417 0.096417 0.186174 19904.0 8.908008 ... 0.15
score_senmayo senscore total_counts \
max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max
Group
Ground Control | 12 Weeks 0.15 31600.0 -0.033288 0.066989 -0.241807 -0.079504 -0.038444 0.006421 0.342407 31600.0 -0.033288 0.066989 -0.241807 -0.079504 -0.038444 0.006421 0.342407 31600.0 2919.924805 792.438599 998.631775 2217.641357 3095.519897 3620.714172 4505.247070
Ground Control | 29 Weeks 0.15 35362.0 -0.015317 0.066006 -0.264210 -0.060767 -0.019144 0.024251 0.465130 35362.0 -0.015317 0.066006 -0.264210 -0.060767 -0.019144 0.024251 0.465130 35362.0 2443.630859 892.928528 791.686646 1679.858307 2301.552124 3228.400818 4571.165527
Space Flight | 12 Weeks 0.15 16408.0 -0.020337 0.069283 -0.254214 -0.069104 -0.024681 0.021868 0.349868 16408.0 -0.020337 0.069283 -0.254214 -0.069104 -0.024681 0.021868 0.349868 16408.0 3050.029053 877.161987 1028.917236 2264.757202 3291.687866 3819.286377 4661.812988
Space Flight | 29 Weeks 0.15 19904.0 -0.014805 0.071641 -0.228862 -0.064321 -0.020246 0.027663 0.343610 19904.0 -0.014805 0.071641 -0.228862 -0.064321 -0.020246 0.027663 0.343610 19904.0 2927.207275 835.011536 834.153625 2128.512390 3238.700073 3646.416016 4650.085938
total_counts_hb total_counts_mt total_counts_ribo
count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max
Group
Ground Control | 12 Weeks 31600.0 0.419395 0.695018 0.0 0.0 0.0 0.763981 15.948441 31600.0 8.673212 5.263829 0.000000 4.875312 7.473253 11.234807 37.437180 31600.0 15.850301 9.099817 0.0 9.972312 14.025234 19.659446 117.593636
Ground Control | 29 Weeks 35362.0 0.343186 0.723648 0.0 0.0 0.0 0.000000 13.867619 35362.0 9.907805 5.310478 0.000000 6.143592 9.282199 12.854851 38.024384 35362.0 15.092834 13.335827 0.0 5.742307 10.452679 21.477554 147.756897
Space Flight | 12 Weeks 16408.0 0.485886 0.739634 0.0 0.0 0.0 0.837134 14.059389 16408.0 14.301973 6.259934 1.286561 9.453764 13.222489 18.227602 41.484730 16408.0 36.673164 17.590736 0.0 24.099486 34.727049 46.690003 181.415146
Space Flight | 29 Weeks 19904.0 0.489396 0.774680 0.0 0.0 0.0 0.841994 16.229515 19904.0 16.570513 7.799766 0.000000 10.343445 15.495999 21.877836 47.091106 19904.0 38.169167 17.706547 0.0 25.051446 35.702381 48.261718 160.861664
[4 rows x 264 columns]
*** Cell Composition (%) ***
annotation_by_overlap Excitatory Inhibitory Microglial Oligodendrocyte Astrocyte OPC Excitatory-Inhibitory
sample
RRRM2_BRN_GC_ISS-T_YNG_GY4 33.76 30.25 3.95 16.18 11.46 2.52 1.88
RRRM2_BRN_GC_ISS-T_YNG_GY9 26.42 37.53 4.21 16.60 10.01 3.60 1.63
RRRM2_BRN_GC_ISS-T_OLD_GO18 32.16 37.08 4.74 13.69 9.28 3.05 0.00
RRRM2_BRN_FLT_ISS-T_OLD_FO20 31.82 25.68 4.37 25.25 8.19 3.51 1.18
RRRM2_BRN_GC_ISS-T_OLD_GO19 26.29 40.30 4.39 18.09 7.52 3.38 0.03
RRRM2_BRN_GC_ISS-T_OLD_GO13 19.81 30.43 3.50 27.48 11.52 3.70 3.56
RRRM2_BRN_FLT_ISS-T_YNG_FY8 15.45 39.16 5.71 23.49 8.85 4.30 3.04
RRRM2_BRN_FLT_ISS-T_YNG_FY7 27.12 22.76 5.21 28.56 7.25 3.46 5.64
RRRM2_BRN_FLT_ISS-T_OLD_FO19 43.83 31.15 2.78 13.26 6.51 2.47 0.00
RRRM2_BRN_GC_ISS-T_YNG_GY7 21.42 33.11 4.35 22.92 11.05 3.97 3.17
RRRM2_BRN_FLT_ISS-T_OLD_FO14 39.39 17.65 4.09 23.27 8.83 3.83 2.95
RRRM2_BRN_GC_ISS-T_YNG_GY1 34.20 24.26 4.19 20.89 9.40 3.57 3.49
RRRM2_BRN_FLT_ISS-T_YNG_FY2 37.48 18.23 4.80 24.08 8.28 4.08 3.06
RRRM2_BRN_FLT_ISS-T_OLD_FO17 65.93 16.28 2.27 10.22 3.26 1.59 0.45
RRRM2_BRN_GC_ISS-T_OLD_GO16 77.91 8.15 1.31 6.75 3.56 1.18 1.14
RRRM2_BRN_FLT_ISS-T_OLD_FO16 41.06 21.38 4.40 19.01 7.90 3.21 3.04
RRRM2_BRN_GC_ISS-T_YNG_GY2 30.40 23.36 3.88 24.38 8.90 3.67 5.42
RRRM2_BRN_FLT_ISS-T_YNG_FY5 44.07 21.41 4.65 17.50 7.26 3.70 1.41
Original Cell N N Cells Percent_Filtered
sample
RRRM2_BRN_GC_ISS-T_OLD_GO13 3880 3595 7.35
RRRM2_BRN_GC_ISS-T_YNG_GY1 3968 3672 7.46
RRRM2_BRN_GC_ISS-T_OLD_GO16 20000 18386 8.07
RRRM2_BRN_GC_ISS-T_YNG_GY7 5776 5265 8.85
RRRM2_BRN_FLT_ISS-T_OLD_FO14 4260 3864 9.30
RRRM2_BRN_GC_ISS-T_YNG_GY9 6044 5465 9.58
RRRM2_BRN_FLT_ISS-T_YNG_FY2 4125 3730 9.58
RRRM2_BRN_FLT_ISS-T_YNG_FY8 5658 5095 9.95
RRRM2_BRN_GC_ISS-T_OLD_GO18 6503 5842 10.16
RRRM2_BRN_GC_ISS-T_YNG_GY2 10779 9651 10.46
RRRM2_BRN_GC_ISS-T_OLD_GO19 8476 7539 11.05
RRRM2_BRN_FLT_ISS-T_OLD_FO17 1493 1321 11.52
RRRM2_BRN_GC_ISS-T_YNG_GY4 8542 7547 11.65
RRRM2_BRN_FLT_ISS-T_OLD_FO20 5641 4922 12.75
RRRM2_BRN_FLT_ISS-T_YNG_FY7 4238 3669 13.43
RRRM2_BRN_FLT_ISS-T_OLD_FO16 4863 4050 16.72
RRRM2_BRN_FLT_ISS-T_YNG_FY5 4714 3914 16.97
RRRM2_BRN_FLT_ISS-T_OLD_FO19 7265 5747 20.89
Get Marker Gene Sets¶
From Google Drive, except senmayo derived from file downloaded from https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/SAUL_SEN_MAYO.html then created using:
from pybiomart import Server
senmayo = pd.read_csv("resources/SAUL_SEN_MAYO.v2025.1.Hs.tsv",
sep="\t", header=0)
senmayo = senmayo[senmayo.STANDARD_NAME == "GENE_SYMBOLS"]
senmayo = pd.Series({"Senmayo": senmayo.iloc[0, 1].split(
",")}, name="symbol").rename_axis("Gene_Set").explode().str.strip(
).replace("", np.nan).dropna().to_frame()
server = Server(host="http://www.ensembl.org")
mart = server.marts["ENSEMBL_MART_ENSEMBL"] # Access the Ensembl mart
human_dataset = mart.datasets["hsapiens_gene_ensembl"]
orthologs = human_dataset.query(attributes=[
"external_gene_name", "mmusculus_homolog_associated_gene_name"])
orthologs = orthologs.dropna().set_index(orthologs.columns[0])
orthologs = orthologs.loc[orthologs.index.intersection(
senmayo.symbol.to_list())].iloc[:, 0]
print(f"{orthologs.shape[0]} orthologs found out of"
f" {len(senmayo.symbol.unique())} senmayo genes")
senmayo = senmayo.assign(symbol=senmayo.replace({"symbol": dict(zip(
orthologs.index, orthologs.values))}))
senmayo.to_csv("gene_sets/senmayo.csv")
(Also could use scflow.tl.convert_gene_species(senmayo.symbol.to_list()))
Certain gene sets created from Google Drive ADBR AWG > Projects > White Matter > Gene Sets files (with the suffix = "_adjusted") were modified for consistency, e.g.,
pd.read_csv("gene_sets/_iron_genes_gmt_out.csv", index_col=[
0, 1]).rename_axis(["row_id", "pathway"]).stack().to_frame(
"symbol").reset_index(-1, drop=True).reset_index().to_csv(
"gene_sets/_iron_genes_gmt_out_adjusted.csv")
A Priori¶
In [3]:
mks = [pd.read_csv(os.path.join("gene_sets", i)).dropna(
how="all", axis=1).assign(Source_File=i) for i in os.listdir("gene_sets")]
mks = [x.assign(Gene_Set=x["Source_File"].iloc[0].split("_2025")[0]) if (
"pathway" in x) else x for x in mks]
mks = pd.concat(mks).drop("Unnamed: 0", axis=1).drop("row_id", axis=1)
mks = mks[mks.symbol.isin(self.rna.var_names)]
mks = mks[["Gene_Set", "symbol"]].set_index("Gene_Set").groupby(
"Gene_Set").apply(lambda x: x["symbol"].to_list())
marker_genes_dict = dict(mks)
marker_gene_sets = mks.copy()
print(marker_gene_sets)
Gene_Set GOBP_CELLULAR_RESPONSE_TO_IRON_ION [B2m, Bmp6, Gpld1, Ireb2, Tfrc, Trf] GOBP_CENTRAL_NERVOUS_SYSTEM_MYELIN_FORMATION [Abca2, Ckap5, Cntn1, Ercc2, Mag, Mios, Tenm4] GOBP_INTRACELLULAR_IRON_ION_HOMEOSTASIS [Abcb7, Aco1, Atp13a2, Atp6ap1, Atp6v0a2, Atp6v0d1, Atp6v1a, Atp6v1g1, B2m, Bmp6, Bmyc, Bola2, C... GOBP_IRON_COORDINATION_ENTITY_TRANSPORT [Abcb7, Abcc5, Flvcr1, Pgrmc2, Slc22a17, Slc48a1] GOBP_IRON_ION_IMPORT_ACROSS_PLASMA_MEMBRANE [Iscu, Steap2] GOBP_IRON_ION_TRANSMEMBRANE_TRANSPORT [Abcb7, Abcc5, Atp7a, Hif1a, Iscu, Nos1, Slc11a2, Slc25a28, Slc25a37, Slc39a14, Slc48a1, Steap2] GOBP_IRON_ION_TRANSPORT [Abcb7, Abcc5, Arhgap1, Atp7a, B2m, Cltc, Dnm2, Flvcr1, Fth1, Heph, Hif1a, Iscu, Lmtk2, Mmgt1, M... GOBP_MULTICELLULAR_ORGANISMAL_LEVEL_IRON_ION_HOMEOSTASIS [Ank1, B2m, Bmp6, Btbd9, Eif2ak1, Epas1, Fbxl5, Fech, Htt, Ireb2, Neo1, Picalm, Slc11a2, Sod2] GOBP_MYELIN_ASSEMBLY [Abca2, Cd9, Ckap5, Cntn1, Cntnap1, Dicer1, Epb41l3, Ercc2, Fig4, Gnpat, Gpc1, Ilk, Mag, Mios, M... GOBP_MYELIN_MAINTENANCE [Abcd1, Abcd2, Akt1, Akt2, Clu, Degs1, Epb41l3, Fa2h, Myrf, Ndrg1, Pals1, Plec, Pten, Sh3tc2, Sod1] GOBP_NEGATIVE_REGULATION_OF_IRON_ION_TRANSMEMBRANE_TRANSPORT [Atp7a, Iscu, Nos1] GOBP_NEGATIVE_REGULATION_OF_IRON_ION_TRANSPORT [Atp7a, Iscu, Nos1] GOBP_REGULATION_OF_IRON_ION_TRANSMEMBRANE_TRANSPORT [Atp7a, Iscu, Nos1] GOBP_REGULATION_OF_IRON_ION_TRANSPORT [Atp7a, B2m, Iscu, Nos1, Trf] GOBP_REGULATION_OF_MYELINATION [Akt1, Cdh2, Ctnnb1, Cyfip1, Dag1, Dicer1, Dlg1, Eif2ak3, Fig4, Hes5, Hgf, Hnrnpk, Igf1, Jam2, L... GOBP_RESPONSE_TO_IRON_ION [Abat, Aco1, B2m, Bcl2, Bmp6, Gpld1, Ireb2, Snca, Tfrc, Trf] GOBP_SEQUESTERING_OF_IRON_ION [Fth1, Ftl1] GOBP_SPHINGOMYELIN_BIOSYNTHETIC_PROCESS [Abca8a, Abca8b, Ormdl3, Osbp, Samd8, Sgms1, Sptlc1, Sptlc2, Vapa] GOBP_SPHINGOMYELIN_CATABOLIC_PROCESS [Prkcd, Smpd1, Smpd3, Smpd4, Smpdl3a, Smpdl3b] GOBP_SPHINGOMYELIN_METABOLIC_PROCESS [Abca2, Abca8a, Abca8b, Ormdl3, Osbp, Prkcd, Samd8, Sgms1, Smpd1, Smpd3, Smpd4, Smpdl3a, Smpdl3b... GOMF_FERRIC_IRON_BINDING [Fth1, Ftl1, Trf] GOMF_FERROUS_IRON_BINDING [Alkbh1, Alkbh3, Cdo1, Dnajc24, Egln1, Egln2, Fth1, Ftl1, Fto, Heph, Hif1an, Iscu, Nt5e, Phyh, P... GOMF_FERROUS_IRON_TRANSMEMBRANE_TRANSPORTER_ACTIVITY [Mmgt1, Slc11a2, Slc25a28, Slc25a37, Slc39a14] GOMF_IRON_ION_BINDING [Abce1, Aco2, Ado, Agmo, Alkbh1, Alkbh3, Alkbh8, Calr, Cdo1, Ciapin1, Cisd1, Cygb, Cyp11a1, Cyp2... GOMF_IRON_ION_TRANSMEMBRANE_TRANSPORTER_ACTIVITY [Mmgt1, Slc11a2, Slc25a28, Slc25a37, Slc39a14, Tfrc] GOMF_OXIDOREDUCTASE_ACTIVITY_ACTING_ON_PAIRED_DONORS_WITH_INCORPORATION_OR_REDUCTION_OF_MOLECULAR_OXYGEN_REDUCED_IRON_SULFUR_PROTEIN_AS_ONE_DONOR_AND_INCORPORATION_OF_ONE_ATOM_OF_OXYGEN [Ahr, Cyp11a1, Cyp2u1, Fdx1] GOMF_SPHINGOMYELIN_PHOSPHODIESTERASE_ACTIVITY [Smpd1, Smpd3, Smpd4, Smpdl3a, Smpdl3b] GOMF_STRUCTURAL_CONSTITUENT_OF_MYELIN_SHEATH [Gpm6b, Mal, Mal2, Mbp, Mobp, Pllp, Plp1] REACTOME_IRON_UPTAKE_AND_TRANSPORT [Aco1, Atp6ap1, Atp6v0a1, Atp6v0a2, Atp6v0a4, Atp6v0b, Atp6v0c, Atp6v0d1, Atp6v0e, Atp6v0e2, Atp... REACTOME_MITOCHONDRIAL_IRON_SULFUR_CLUSTER_BIOGENESIS [Fdx1, Fdxr, Glrx5, Hscb, Isca1, Isca2, Iscu, Lyrm4, Nfs1] Senmayo [Acvr1b, Angpt1, Bex3, Bmp6, Cd9, Csf1, Ctnnb1, Ctsb, Egf, Egfr, Ets2, Fgf1, Hgf, Hmgb1, Igf1, I... WP_IRON_HOMEOSTASIS [Fth1, Ftl1, Ireb2, Trf] dtype: object
Clusters in Data¶
In [4]:
self.find_markers(col_celltype=col_celltype)
df_celltype_markers = self.get_markers_df(
n_genes=50, col_celltype=col_celltype,
log2fc_threshold=1, p_threshold=1e-10)
df_celltype_markers.groupby(col_celltype).apply(lambda x: x.head(3))
Out[4]:
| scores | logfoldchanges | pvals | pvals_adj | |||
|---|---|---|---|---|---|---|
| annotation_by_overlap | annotation_by_overlap | names | ||||
| Astrocyte | Astrocyte | Nim1k | 47.064068 | 2.660807 | 0.0 | 0.0 |
| Pbxip1 | 45.993198 | 4.411093 | 0.0 | 0.0 | ||
| Aldh1l1 | 46.067970 | 4.603257 | 0.0 | 0.0 | ||
| Excitatory | Excitatory | Slc8a2 | 59.332294 | 1.443789 | 0.0 | 0.0 |
| Gm37407 | 57.852417 | 1.410103 | 0.0 | 0.0 | ||
| Kcnh3 | 57.889107 | 1.536317 | 0.0 | 0.0 | ||
| Excitatory-Inhibitory | Excitatory-Inhibitory | Megf9 | 52.264030 | 1.840525 | 0.0 | 0.0 |
| Rasgrf1 | 51.559391 | 1.407420 | 0.0 | 0.0 | ||
| Scn8a | 51.671570 | 1.217524 | 0.0 | 0.0 | ||
| Inhibitory | Inhibitory | Zmym3 | 51.089523 | 1.472235 | 0.0 | 0.0 |
| Hrh3 | 51.470680 | 1.819407 | 0.0 | 0.0 | ||
| N4bp2l1 | 51.454659 | 1.068241 | 0.0 | 0.0 | ||
| Microglial | Microglial | Srgap2 | 125.218414 | 4.258848 | 0.0 | 0.0 |
| Tgfbr1 | 124.597107 | 7.077548 | 0.0 | 0.0 | ||
| Slco2b1 | 64.663284 | 8.772433 | 0.0 | 0.0 | ||
| OPC | OPC | Brinp3 | 140.637009 | 3.586013 | 0.0 | 0.0 |
| Grid2 | 134.924301 | 2.397604 | 0.0 | 0.0 | ||
| Epn2 | 126.522072 | 3.946976 | 0.0 | 0.0 | ||
| Oligodendrocyte | Oligodendrocyte | Hcn2 | 55.754898 | 2.162222 | 0.0 | 0.0 |
| Ankib1 | 53.085659 | 1.270694 | 0.0 | 0.0 | ||
| Tmem229a | 52.977585 | 3.292466 | 0.0 | 0.0 |
Score Senescence¶
See Available Hubs¶
In [5]:
hubs = senepy.load_hubs(species=species)
hubs.metadata.set_index(["tissue", "cell"]).sort_index()
Out[5]:
| hub_num | size | n_sen | hyp | ||
|---|---|---|---|---|---|
| tissue | cell | ||||
| Bladder | bladder cell | 0 | 36 | 0 | 1.000000e+00 |
| bladder cell | 1 | 20 | 0 | 1.000000e+00 | |
| bladder cell | 2 | 67 | 3 | 2.352239e-02 | |
| endothelial cell | 3 | 5 | 1 | 4.491007e-02 | |
| Brain | microglial cell | 1 | 59 | 1 | 4.189625e-01 |
| microglial cell | 2 | 22 | 2 | 1.704626e-02 | |
| Diaphragm | mesenchymal stem cell | 0 | 247 | 6 | 2.634383e-02 |
| skeletal muscle satellite cell | 0 | 60 | 0 | 1.000000e+00 | |
| skeletal muscle satellite cell | 1 | 375 | 14 | 9.664211e-06 | |
| Fat | endothelial cell | 0 | 416 | 12 | 4.660581e-04 |
| mesenchymal stem cell | 0 | 38 | 0 | 1.000000e+00 | |
| mesenchymal stem cell | 1 | 29 | 0 | 1.000000e+00 | |
| mesenchymal stem cell | 2 | 44 | 2 | 6.129178e-02 | |
| HSC | hematopoietic stem cell | 1 | 56 | 1 | 4.026751e-01 |
| hematopoietic stem cell | 2 | 172 | 1 | 7.955235e-01 | |
| hematopoietic stem cell | 3 | 77 | 0 | 1.000000e+00 | |
| Heart_and_Aorta | endocardial cell | 0 | 171 | 17 | 3.456117e-13 |
| endocardial cell | 1 | 180 | 3 | 2.278544e-01 | |
| endothelial cell | 0 | 4 | 1 | 3.609150e-02 | |
| endothelial cell | 1 | 41 | 3 | 6.217995e-03 | |
| fibroblast | 0 | 80 | 5 | 8.372629e-04 | |
| fibroblast | 1 | 50 | 2 | 7.663093e-02 | |
| myocyte | 0 | 325 | 4 | 3.465634e-01 | |
| myocyte | 1 | 95 | 10 | 1.655335e-08 | |
| smooth muscle cell | 0 | 120 | 5 | 4.954367e-03 | |
| Kidney | Henle thick ascending limb epithelial cell | 0 | 765 | 20 | 2.476907e-05 |
| collecting duct principal cell | 0 | 1323 | 20 | 1.865129e-02 | |
| distal convoluted tubule epithelial cell | 0 | 330 | 2 | 8.074111e-01 | |
| epithelial cell of proximal tubule | 1 | 53 | 3 | 1.261710e-02 | |
| fenestrated cell | 0 | 131 | 4 | 3.239778e-02 | |
| macrophage | 0 | 365 | 12 | 1.418130e-04 | |
| podocyte | 0 | 315 | 9 | 2.531397e-03 | |
| proximal convoluted tubule epithelial cell | 0 | 34 | 0 | 1.000000e+00 | |
| proximal convoluted tubule epithelial cell | 1 | 31 | 3 | 2.803020e-03 | |
| Large_Intestine | epithelial cell | 0 | 942 | 7 | 7.637002e-01 |
| goblet cell | 1 | 36 | 0 | 1.000000e+00 | |
| secretory cell | 0 | 34 | 0 | 1.000000e+00 | |
| Limb_Muscle | macrophage | 0 | 184 | 2 | 5.034232e-01 |
| macrophage | 1 | 45 | 1 | 3.389737e-01 | |
| skeletal muscle satellite cell | 0 | 27 | 0 | 1.000000e+00 | |
| Liver | Kupffer cell | 0 | 638 | 14 | 2.268194e-03 |
| Kupffer cell | 1 | 3305 | 17 | 9.983103e-01 | |
| endothelial cell of hepatic sinusoid | 0 | 205 | 12 | 4.280991e-07 | |
| hepatocyte | 0 | 1600 | 14 | 6.086587e-01 | |
| hepatocyte | 1 | 143 | 7 | 3.511115e-04 | |
| Lung | alveolar macrophage | 1 | 37 | 0 | 1.000000e+00 |
| fibroblast | 0 | 244 | 2 | 6.562770e-01 | |
| fibroblast | 1 | 552 | 21 | 3.756258e-08 | |
| Lymphoid | B cell | 0 | 27 | 0 | 1.000000e+00 |
| B cell | 1 | 167 | 4 | 6.748011e-02 | |
| NK cell | 1 | 31 | 0 | 1.000000e+00 | |
| NK cell | 2 | 64 | 3 | 2.086629e-02 | |
| T cell | 0 | 54 | 0 | 1.000000e+00 | |
| T cell | 1 | 159 | 7 | 6.629509e-04 | |
| T cell | 2 | 35 | 2 | 4.064697e-02 | |
| Myeloid | macrophage | 0 | 77 | 0 | 1.000000e+00 |
| macrophage | 1 | 57 | 1 | 4.081541e-01 | |
| monocyte | 0 | 67 | 0 | 1.000000e+00 | |
| monocyte | 1 | 14 | 2 | 7.044369e-03 | |
| Pancreas | pancreatic A cell | 0 | 135 | 4 | 3.559225e-02 |
| Thymus | thymocyte | 0 | 680 | 13 | 9.917213e-03 |
| thymocyte | 1 | 411 | 8 | 3.573660e-02 | |
| Tongue | basal cell | 0 | 50 | 1 | 3.687258e-01 |
| basal cell | 1 | 266 | 13 | 1.091835e-06 | |
| keratinocyte | 0 | 197 | 5 | 3.504157e-02 | |
| keratinocyte | 1 | 350 | 16 | 1.499907e-07 | |
| Trachea | basal cell | 0 | 45 | 0 | 1.000000e+00 |
| basal cell | 2 | 289 | 14 | 4.628384e-07 | |
| fibroblast | 0 | 808 | 27 | 5.751673e-09 | |
| fibroblast | 1 | 102 | 2 | 2.394198e-01 | |
| macrophage | 0 | 228 | 1 | 8.784059e-01 | |
| macrophage | 1 | 1105 | 20 | 2.802038e-03 |
Run Scoring¶
If you want just to find the senescence-related genes, run:
tissue = "Brain" # or whatever you want here
celltype = None # or whatever you want here
genes_senepy, hubs, figs = find_senescence_genes(
self.rna, species=species, tissue=tissue, celltype=celltype,
overlap_threshold=0, literature_sources=None,
sengpt_sources=True, plot=True, col_celltype=col_celltype
) # find tissue- and/or cell type-specific genes
Senepy¶
In [6]:
%%time
# tissue = ["Brain", "Myeloid"]
tissue = "Brain"
# tissue = None
# celltype = ["microglia", "t cell", "monocyte", "GABA interneuron"]
# celltype = "microglia"
# celltype = "microglial cell"
celltype = None
if "senepy" in sen_metrics:
self.rna.X = self.rna.layers["log1p"].copy()
self.rna, genes_senepy, figs = run_senepy(
self.rna, species=species, tissue=tissue, celltype=celltype,
overlap_threshold=0, literature_sources=None,
sengpt_sources=True, col_celltype=col_celltype,
col_senscore="score_senepy", identifiers=[col_celltype],
use_translator=True, plot=True, figsize=(20, 20),
swap_axes=False, standard_scale="group", plot_layer="log1p"
) # find tissue- and/or cell type-specific genes
self.rna.obs.loc[
:, "score_senepy_original_scale"] = self.rna.obs.score_senepy
self.rna.obs.loc[:, "score_senepy"] = np.log(
self.rna.obs.score_senepy) # log-transform senescence score
self.rna.obs[["score_senepy"]]
CPU times: user 3 μs, sys: 0 ns, total: 3 μs Wall time: 4.77 μs
Senmayo¶
In [7]:
sc.tl.score_genes(self.rna, marker_gene_sets.loc["Senmayo"],
ctrl_as_ref=True, ctrl_size=50, gene_pool=None,
n_bins=25, score_name="score_senmayo", random_state=0,
copy=False, use_raw=None, layer="log1p")
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/scanpy/tools/_score_genes.py:241: DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be removed in Python 3.16. This returns the bitwise inversion of the underlying int object and is usually not what you expect from negating a bool. Use the 'not' operator for boolean negation or ~int(x) if you really want the bitwise inversion of the underlying int. r_genes: pd.Index[str] = obs_cut[(obs_cut == cut) & ~keep_ctrl_in_obs_cut].index
Label SnCs¶
Define Snc Threshold & Label Cells¶
Based on percentile threshold derived from control group
In [8]:
# Determine Threshold
perc_s, perc = str(100 - percentile) + "%", 1 - (percentile / 100)
p_h = 3
perc_s_h, perc_h = str(100 - p_h) + "%", 1 - (p_h / 100)
print("Percentile for Threshold:", perc_s)
for u in sen_metrics:
self.rna.obs = self.rna.obs.join(self.rna.obs[self.rna.obs[
col_batch] == keys[col_batch]["key_control"]].groupby(
col_celltype).apply(lambda x: x[f"score_{u}"].describe(
percentiles=[perc])[perc_s], include_groups=False).to_frame(
f"Senescence_Threshold_{u}"), on=col_celltype).loc[
self.rna.obs.index]
self.rna.obs = self.rna.obs.join(self.rna.obs[self.rna.obs[
col_batch] == keys[col_batch]["key_control"]].groupby(
col_celltype).apply(lambda x: x[f"score_{u}"].describe(
percentiles=[perc_h])[perc_s_h],
include_groups=False).to_frame(
f"Senescence_Threshold_{u}_{p_h}"),
on=col_celltype).loc[self.rna.obs.index]
# Binary CLassification
for x in sen_metrics:
self.rna.obs.loc[:, f"Senescent_Cell_{x}"] = (self.rna.obs[
f"score_{x}"] >= self.rna.obs[f"Senescence_Threshold_{x}"])
self.rna.obs.loc[:, f"Senescent_Cell_{x}_{p_h}"] = (self.rna.obs[
f"score_{x}"] >= self.rna.obs[f"Senescence_Threshold_{x}_{p_h}"])
# String Labels (for Plotting/Categorical Analysis)
for x in sen_metrics:
self.rna.obs.loc[:, f"Senescent_Cell_Label_{x}"] = self.rna.obs[
f"Senescent_Cell_{x}"].astype(bool).astype(str).replace({
"True": "Senescent", "False": "Non-Senescent"})
self.rna.obs.loc[:, f"Senescent_Cell_Label_hierarchy_{x}"] = self.rna.obs[
f"Senescent_Cell_{x}"].astype(bool).astype(str).replace({
"True": "Senescent", "False": "Non-Senescent"})
self.rna.obs.loc[:, f"Senescent_Cell_Label_{x}_{p_h}"] = self.rna.obs[
f"Senescent_Cell_{x}_{p_h}"].astype(bool).astype(str).replace({
"True": "Senescent", "False": "Non-Senescent"})
self.rna.obs.loc[:, f"Senescent_Cell_Label_by_Type_{x}"] = self.rna.obs[
f"Senescent_Cell_Label_{x}"].replace({
"Senescent": "SnC ", "Non-Senescent": ""}) + self.rna.obs[
col_celltype].astype(str)
self.rna.obs.loc[:, f"SnC_hierarchy_{x}"] = self.rna.obs[
f"Senescent_Cell_Label_{x}"].replace({
"Senescent": "SnC ", "Non-Senescent": ""}) + self.rna.obs[
col_celltype + "_hierarchy"].astype(str)
# Descriptives
for x in sen_metrics:
print(x, round(self.rna.obs[[
f"Senescent_Cell_Label_{x}", col_celltype]].value_counts(
normalize=True).sort_index() * 100, 1).unstack(0))
# Choose Metrics for Main Analysis
for x in ["Senescent_Cell_Label", "Senescent_Cell", "SnC_hierarchy",
"Senescence_Threshold", "Senescent_Cell_Label_by_Type"]:
self.rna.obs.loc[:, x] = self.rna.obs[f"{x}_{use_metric}"]
self.rna.obs.loc[:, "senscore"] = self.rna.obs[f"score_{use_metric}"]
genes = marker_gene_sets.loc["Senmayo"] if (
use_metric == "senmayo") else genes_senepy
Percentile for Threshold: 99% senmayo Senescent_Cell_Label_senmayo Non-Senescent Senescent annotation_by_overlap Excitatory 39.3 0.6 Inhibitory 24.9 0.4 Microglial 3.7 0.1 Oligodendrocyte 17.6 0.3 Astrocyte 7.8 0.1 OPC 2.9 0.1 Excitatory-Inhibitory 2.1 0.0
Label Batch/Cell-Specific Senescence Percentiles¶
In [9]:
# for x in ["Senescence_Label", "Senescence_CellType"]:
# if x in self.rna.obs:
# self.rna.obs = self.rna.obs.drop(x, axis=1)
# # %ile Thresholds (Overall)
# thresh = self.rna.obs["score_senepy"].describe(
# percentiles=[0.90, 0.95, 0.99])
# thres_ct = self.rna.obs[["score_senepy", col_celltype]].groupby(
# col_celltype).describe(percentiles=[0.90, 0.95, 0.99])["score_senepy"]
# sen = self.rna.obs["score_senepy"].apply(
# lambda x: "Top 1%" if x >= float(thresh.loc["99%"]) else "Top 5%" if (
# x >= float(thresh.loc["95%"])) else "Top 10%" if x >= float(
# thresh.loc["90%"]) else "Bottom 90%").to_frame(
# "Senescence_Label").loc[self.rna.obs.index]
# self.rna.obs = self.rna.obs.join(sen).loc[self.rna.obs.index]
# # %ile Thresholds (by Cell Type)
# sct = self.rna.obs.groupby(
# col_celltype).apply(lambda g: g["score_senepy"].apply(
# lambda x: f"Top 1% {g.name}" if x >= float(thres_ct.loc[g.name][
# "99%"]) else f"Top 5% {g.name}" if (x >= float(thres_ct.loc[
# g.name]["95%"])) else f"Top 10% {g.name}" if x >= float(
# thres_ct.loc[g.name]["90%"]) else g.name),
# include_groups=False)
# sct = sct.to_frame("Senescence_CellType").reset_index(0, drop=True)
# sct = sct.loc[self.rna.obs.index]
# self.rna.obs = self.rna.obs.join(sct).loc[self.rna.obs.index]
# for x in ["Senescence_Label", "Senescence_CellType"]:
# self.rna.obs = self.rna.obs.assign(**{
# x: self.rna.obs[x].astype("category")})
# high_sen_cts = [i for i in self.rna.obs["Senescence_CellType"].unique(
# ) if " 1% " in i]
Descriptives of SnCs¶
In [10]:
print("Percent Senescent: \n\n", round(self.rna.obs.groupby([
col_celltype, col_batch]).apply(lambda x: x["Senescent_Cell"].astype(
int).value_counts(normalize=True)[1], include_groups=False) * 100,
2).unstack(col_celltype))
sns.catplot(self.rna.obs, y="Senescent_Cell", x=col_celltype,
hue=col_batch, kind="bar", height=10)
Percent Senescent: annotation_by_overlap Excitatory Inhibitory Microglial Oligodendrocyte Astrocyte OPC Excitatory-Inhibitory Group Ground Control | 12 Weeks 1.01 1.01 1.01 1.01 1.00 1.02 1.05 Ground Control | 29 Weeks 1.73 1.76 1.85 1.27 0.96 2.55 2.35 Space Flight | 12 Weeks 0.65 2.58 1.66 2.09 0.46 3.73 3.77 Space Flight | 29 Weeks 2.33 1.87 2.56 1.84 1.96 3.75 1.00
Out[10]:
<seaborn.axisgrid.FacetGrid at 0x73c607a50ad0>
SnC DEGs¶
In [ ]:
self.find_markers(col_celltype="Senescent_Cell_Label_by_Type")
df_snc_markers = self.get_markers_df(
n_genes=50, col_celltype="Senescent_Cell_Label_by_Type",
log2fc_threshold=1, p_threshold=1e-10)
print(df_snc_markers.loc[pd.unique([
i[0] for i in df_snc_markers.index.values if ("SnC" in i[0])])].groupby(
"Senescent_Cell_Label_by_Type").apply(lambda x: x.head(3)))
for x in self.rna.obs[col_celltype].unique():
Out[ ]:
| scores | logfoldchanges | pvals | pvals_adj | |||
|---|---|---|---|---|---|---|
| Senescent_Cell_Label_by_Type | Senescent_Cell_Label_by_Type | names | ||||
| SnC Astrocyte | SnC Astrocyte | Zbtb20 | 27.045067 | 2.925831 | 2.509168e-44 | 2.062052e-43 |
| Ptn | 12.652748 | 3.181505 | 1.966155e-21 | 1.410711e-20 | ||
| Nfia | 12.566462 | 2.662537 | 2.847718e-21 | 2.040809e-20 | ||
| SnC Excitatory | SnC Excitatory | Nrg1 | 39.431129 | 2.263098 | 1.104108e-176 | 1.335639e-173 |
| Meg3 | 17.437487 | 1.237427 | 2.882203e-56 | 1.025471e-53 | ||
| Kcnip4 | 12.480145 | 1.157420 | 3.237619e-32 | 2.543213e-30 | ||
| SnC Excitatory-Inhibitory | SnC Excitatory-Inhibitory | Ntng1 | 71.276108 | 5.574868 | 5.427843e-45 | 1.265137e-43 |
| Meg3 | 31.826820 | 2.392547 | 5.288524e-31 | 1.213952e-29 | ||
| Snhg11 | 31.340244 | 2.166353 | 9.192460e-31 | 2.106083e-29 | ||
| SnC Inhibitory | SnC Inhibitory | Meg3 | 46.909836 | 1.721173 | 1.207903e-180 | 4.058889e-178 |
| Cntnap2 | 32.584690 | 2.046545 | 5.359733e-120 | 1.117874e-117 | ||
| Snhg11 | 30.802677 | 1.519162 | 3.577045e-113 | 6.761175e-111 | ||
| SnC Microglial | SnC Microglial | Cst3 | 44.654442 | 6.191420 | 1.530109e-49 | 5.533552e-49 |
| C1qb | 32.851158 | 8.059837 | 2.288986e-41 | 8.175335e-41 | ||
| Srgap2 | 27.586052 | 4.312108 | 6.239606e-37 | 2.203167e-36 | ||
| SnC OPC | SnC OPC | Vcan | 34.304527 | 6.757289 | 1.943715e-48 | 2.060747e-47 |
| Lsamp | 33.169975 | 2.359746 | 4.641531e-48 | 4.908094e-47 | ||
| Lhfpl3 | 31.666264 | 4.883138 | 5.424560e-46 | 5.716106e-45 | ||
| SnC Oligodendrocyte | SnC Oligodendrocyte | Plp1 | 118.745720 | 5.659093 | 9.354708e-248 | 9.573934e-247 |
| Mbp | 64.255020 | 4.209384 | 1.718122e-168 | 1.665394e-167 | ||
| Nkain2 | 55.394459 | 2.498176 | 2.500771e-154 | 2.345102e-153 |
Plot¶
Comparison¶
In [241]:
scores = self.rna.obs[[col_batch] + [
f"score_{x}" for x in sen_metrics]].set_index(
col_batch, append=True).reset_index()
sns.pairplot(scores, diag_kind="kde", diag_kws=dict(
cut=0, fill=True, common_norm=True),
hue=col_batch, palette=palette[col_batch])
Out[241]:
<seaborn.axisgrid.PairGrid at 0x77a44d9c1d30>
Scores¶
In [246]:
# UMAP with Senescence Scores
bnds = self.rna.obs["senscore"].describe(percentiles=[0.75, 0.99])
_ = self.plot(kind="umap", color=["senscore", col_celltype], umap=dict(
palette=None, color_map="Reds", vmin=bnds["75%"], vmax=bnds["99%"]))
# Violin Plot of Senescence Scores
_ = self.plot(kind="violin", genes=["senscore"], common_norm=True,
col_celltype=col_celltype, rotation=90)
_ = self.plot(kind="violin", genes=["senscore"], common_norm=True,
by_group=col_celltype, col_wrap=1,
col_celltype=col_batch, rotation=90)
# KDEs
fig_sen_kde = {}
for x in [col_age, col_condition, col_batch]:
fig_sen_kde[x] = sns.displot(self.rna.obs, hue=x, x="senscore",
palette=palette[x], kind="kde",
cut=0, common_norm=True, fill=True)
# Overall (All Cell Types)
fig_sen_overall = sns.catplot(self.rna.obs, x=col_condition,
hue=col_age, y="senscore",
palette=palette[col_age],
kind="violin")
fig_sen_overall_2 = sns.catplot(self.rna.obs, x=col_age, hue=col_condition,
palette=palette[col_condition],
y="senscore", kind="violin")
# By Cell Type
fig_sen_rc = sns.catplot(self.rna.obs, col=col_celltype, y="senscore",
palette=palette[col_condition],
hue=col_condition, x=col_age, kind="violin",
height=10, aspect=2, col_wrap=3)
fig_sen = sns.catplot(self.rna.obs, x=col_celltype, y="senscore",
palette=palette[col_batch],
hue=col_batch, kind="box", height=10, aspect=2)
for a in fig_sen.axes.flatten():
a.tick_params(axis="x", labelrotation=90)
fig_sen_2 = sns.catplot(self.rna.obs, x=col_celltype, y="senscore",
col=col_age, hue=col_condition,
palette=palette[col_condition],
kind="box", height=10, aspect=2)
for a in fig_sen_2.axes.flatten():
a.tick_params(axis="x", labelrotation=90)
fig_sen_3 = sns.catplot(self.rna.obs, x=col_celltype, y="senscore",
col=col_condition, hue=col_age,
palette=palette[col_age],
kind="box", height=10, aspect=2)
for a in fig_sen_3.axes.flatten():
a.tick_params(axis="x", labelrotation=90)
None
<Figure size 640x480 with 0 Axes>
Excitatory
Excitatory-Inhibitory
Oligodendrocyte
Inhibitory
Microglial
OPC
Astrocyte
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
Burden¶
Group by sample and cell type and get percentage of senescent cells
In [12]:
grps = [col_sample, col_celltype, col_age, col_condition, col_batch]
dff = self.rna.obs[grps + ["Senescent_Cell"]].groupby(grps).apply(
lambda x: x["Senescent_Cell"].mean(), include_groups=False
).to_frame("Senescent_Cell_Burden") * 100
# kws_plot = dict(kind="violin", split=False, , common_norm=True)
kws_plot = dict(kind="box", errorbar=("ci", 95))
sns.catplot(y="Senescent_Cell_Burden", x=col_condition, **kws_plot,
palette=palette[col_condition],
hue=col_condition, data=dff, height=3, aspect=1.5)
sns.catplot(y="Senescent_Cell_Burden", x=col_condition, **kws_plot,
col=col_celltype, col_wrap=4,
palette=palette[col_age],
hue=col_age, data=dff, height=3, aspect=1.5)
sns.catplot(y="Senescent_Cell_Burden", hue=col_condition, **kws_plot,
col=col_celltype, col_wrap=4,
palette=palette[col_condition],
x=col_age, data=dff, height=3, aspect=1.5)
sns.catplot(y="Senescent_Cell_Burden", x=col_celltype, **kws_plot,
palette=palette[col_batch],
hue=col_batch, data=dff, height=8, aspect=1.5)
for x in [col_condition, col_age, [col_condition, col_age]]:
print("\n\n", round(dff["Senescent_Cell_Burden"].groupby(x).describe()[[
"min", "25%", "50%", "75%", "max"]], 1))
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
min 25% 50% 75% max
Condition
Ground Control 0.0 0.4 1.0 1.9 6.0
Space Flight 0.0 0.8 1.8 2.7 8.0
min 25% 50% 75% max
Age_End
20 Weeks 0.0 0.4 1.2 2.0 5.8
37 Weeks 0.0 0.5 1.6 2.3 8.0
min 25% 50% 75% max
Condition Age_End
Ground Control 20 Weeks 0.0 0.3 0.8 1.7 4.5
37 Weeks 0.0 0.5 1.2 2.3 6.0
Space Flight 20 Weeks 0.0 0.8 1.9 3.2 5.8
37 Weeks 0.0 0.8 1.7 2.3 8.0
Analyze¶
Regressions & ANOVAs & MEMs¶
In [13]:
r_dff = polars.DataFrame(dff.reset_index()) # pymer-compatible aggregated df
r_rna = polars.DataFrame(self.rna.obs.copy()) # pymer-compatible cell-level df
lvls = ["key_control", "key_treatment"]
factors = {col_condition: [keys[col_condition][i] for i in lvls],
col_age: [keys[col_age][i] for i in lvls]}
Scores¶
In [17]:
# # Age + Condition
# model_ca_mlm = lmer(f"senscore ~ {col_condition} + {col_age} +"
# f"(1 | {col_sample})", data=r_rna) # setup model
# model_ca_mlm.set_factors(factors)
# model_ca_mlm.set_transforms({"senscore": "zscore"})
# model_ca_mlm.anova(summary=True).show()
# # Age * Condition
# model_ca_mlm_i = lmer(
# f"senscore ~ {col_condition} * {col_age} + "
# f"(1 | {col_sample})", data=r_rna) # setup model
# model_ca_mlm_i.set_factors(factors)
# model_ca_mlm_i.set_transforms({"senscore": "zscore"})
# model_ca_mlm_i.anova(summary=True).show()
# # Age + Condition + Cell Type
# model_cac_mlm = lmer(
# f"senscore ~ {col_age} + {col_condition} + {col_celltype} + "
# f"(1 | {col_sample})", data=r_rna) # setup model
# model_cac_mlm.set_factors(factors)
# model_cac_mlm.set_transforms({"senscore": "zscore"})
# model_cac_mlm.anova(summary=True).show()
# # Age + Condition * Cell Type
# model_cac_mlm_i = lmer(
# f"senscore ~ {col_age} + {col_condition} * {col_celltype} + "
# f"(1 | {col_sample})", data=r_rna) # setup model
# model_cac_mlm_i.set_factors(factors)
# model_cac_mlm_i.set_transforms({"senscore": "zscore"})
# model_cac_mlm_i.anova(summary=True).show()
# # print(model_cac_mlm_i.report())
# res_em = model_cac_mlm_i.emmeans(col_condition, by=col_celltype)
# res_em = pd.DataFrame(res_em, columns=res_em.columns).set_index(
# res_em.columns[:2]).map(lambda x: x if isinstance(x, str) or np.isinf(
# x) else round(x, 2))
# res_em = res_em.drop(["asymp_LCL", "asymp_UCL"], axis=1).join(res_em[[
# "asymp_LCL", "asymp_UCL"]].apply(
# lambda x: f"[{x.iloc[0]}, {x.iloc[1]}]", axis=1).to_frame("CI"))
# print(res_em.unstack(col_condition))
# Age * Condition * Cell Type
model_cac_mlm_i3 = lmer(
f"senscore ~ {col_age} * {col_condition} * {col_celltype} + "
f"(1 | {col_sample})", data=r_rna) # setup model
model_cac_mlm_i3.set_factors(factors)
model_cac_mlm_i3.set_transforms({"senscore": "zscore"})
model_cac_mlm_i3.anova(summary=True).show()
# Age * Condition (Condition as Random Effect)
model_ca_mlm_ii = lmer(
f"senscore ~ {col_condition} * {col_age} + "
f"(1 | {col_sample}) + (1 + {col_condition} | {col_celltype})",
data=r_rna) # setup model
model_ca_mlm_ii.set_factors(factors)
model_ca_mlm_ii.set_transforms({"senscore": "zscore"})
model_ca_mlm_ii.anova(summary=True).show()
# Age * Condition (Age and Condition as Random Effect)
model_ca_mlm_iii = lmer(
f"senscore ~ {col_condition} * {col_age} + (1 | {col_sample})"
f" + (1 + {col_condition} + {col_age} | {col_celltype})",
data=r_rna) # setup model
model_ca_mlm_iii.set_factors(factors)
model_ca_mlm_iii.set_transforms({"senscore": "zscore"})
model_ca_mlm_iii.anova(summary=True).show()
# Compare
compare(
# model_ca_mlm, model_ca_mlm_i, model_cac_mlm, model_cac_mlm_i,
model_cac_mlm_i3, model_ca_mlm_ii, model_ca_mlm_iii).show()
# GLM Version of Results
# model_ca_mlm_i.summary().show()
# model_cac_mlm_i.summary().show()
model_cac_mlm_i3.summary().show()
model_ca_mlm_ii.summary().show()
model_ca_mlm_iii.summary().show()
| ANOVA (Type III tests) | ||||||
| model term | df1 | df2 | F_ratio | Chisq | p_value | |
|---|---|---|---|---|---|---|
| Age_End | 1.000 | inf | 2.781 | 2.781 | 0.09538 | . |
| Condition | 1.000 | inf | 11.624 | 11.624 | <.001 | *** |
| annotation_by_overlap | 6.000 | inf | 2,007.061 | 12,042.366 | *** | |
| Age_End:Condition | 1.000 | inf | 0.000 | 0.000 | 0.9975 | |
| Age_End:annotation_by_overlap | 6.000 | inf | 13.785 | 82.710 | <.001 | *** |
| Condition:annotation_by_overlap | 6.000 | inf | 52.540 | 315.240 | <.001 | *** |
| Age_End:Condition:annotation_by_overlap | 6.000 | inf | 14.504 | 87.024 | <.001 | *** |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||
| ANOVA (Type III tests) | ||||||
| model term | df1 | df2 | F_ratio | Chisq | p_value | |
|---|---|---|---|---|---|---|
| Condition | 1.000 | inf | 5.989 | 5.989 | 0.0144 | * |
| Age_End | 1.000 | inf | 3.628 | 3.628 | 0.05683 | . |
| Condition:Age_End | 1.000 | inf | 0.267 | 0.267 | 0.6053 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||
R messages: Note: D.f. calculations have been disabled because the number of observations exceeds 3000. To enable adjustments, add the argument 'lmerTest.limit = 103274' (or larger) [or, globally, 'set emm_options(lmerTest.limit = 103274)' or larger]; but be warned that this may result in large computation time and memory use. R messages: Note: D.f. calculations have been disabled because the number of observations exceeds 3000. To enable adjustments, add the argument 'lmerTest.limit = 103274' (or larger) [or, globally, 'set emm_options(lmerTest.limit = 103274)' or larger]; but be warned that this may result in large computation time and memory use.
| ANOVA (Type III tests) | ||||||
| model term | df1 | df2 | F_ratio | Chisq | p_value | |
|---|---|---|---|---|---|---|
| Condition | 1.000 | inf | 6.266 | 6.266 | 0.01231 | * |
| Age_End | 1.000 | inf | 2.311 | 2.311 | 0.1284 | |
| Condition:Age_End | 1.000 | inf | 0.254 | 0.254 | 0.6145 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||
| Analysis of Deviance Table | |||||||||
| Model 1: lmer(senscore~Age_EndConditionannotation_by_overlap+(1|sample)) Model 2: lmer(senscore~ConditionAge_End+(1|sample)+(1+Condition|annotation_by_overlap)) Model 3: lmer(senscore~ConditionAge_End+(1|sample)+(1+Condition+Age_End|annotation_by_overlap)) |
|||||||||
| AIC | BIC | logLik | npar | -2*log(L) | Chisq | Df | Pr(>Chisq) | ||
|---|---|---|---|---|---|---|---|---|---|
| 1.00 | 277483.5 | 277769.8 | −138711.7 | 9.00 | 277.52K | ||||
| 2.00 | 277548.2 | 277634.1 | −138765.1 | 12.00 | 277.45K | 65.46 | 3.00 | <.001 | *** |
| 3.00 | 277488.8 | 277603.3 | −138732.4 | 30.00 | 277.28K | 177.96 | 18.00 | <.001 | *** |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
| Formula: lmer(senscore~Age_End*Condition*annotation_by_overlap+(1|sample)) | |||||||||
| Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -138711 AIC: 277483 | BIC: 277769 Residual error: 0.926 |
|||||||||
| Random Effects: | Estimate | SE | CI-low | CI-high | T-stat | DF | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.156 | |||||||
| Residual-sd | Observation | 0.926 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | −0.075 | 0.070 | −0.226 | 0.076 | −1.070 | 14.224 | 0.3025 | ||
| Age_End37 Weeks | 0.230 | 0.106 | 0.004 | 0.456 | 2.178 | 14.129 | 0.04687 | * | |
| ConditionSpace Flight | 0.121 | 0.106 | −0.105 | 0.348 | 1.144 | 14.375 | 0.2713 | ||
| annotation_by_overlapInhibitory | −0.245 | 0.014 | −0.272 | −0.218 | −17.907 | 103,243.694 | <.001 | *** | |
| annotation_by_overlapMicroglial | 0.731 | 0.028 | 0.677 | 0.785 | 26.502 | 103,233.445 | <.001 | *** | |
| annotation_by_overlapOligodendrocyte | −0.450 | 0.015 | −0.480 | −0.421 | −29.905 | 103,239.753 | <.001 | *** | |
| annotation_by_overlapAstrocyte | 0.490 | 0.019 | 0.453 | 0.527 | 25.775 | 103,235.512 | <.001 | *** | |
| annotation_by_overlapOPC | 0.132 | 0.030 | 0.074 | 0.191 | 4.444 | 103,234.599 | <.001 | *** | |
| annotation_by_overlapExcitatory-Inhibitory | −0.633 | 0.030 | −0.692 | −0.573 | −20.904 | 103,237.636 | <.001 | *** | |
| Age_End37 Weeks:ConditionSpace Flight | −0.083 | 0.150 | −0.403 | 0.237 | −0.554 | 14.273 | 0.5879 | ||
| Age_End37 Weeks:annotation_by_overlapInhibitory | −0.019 | 0.020 | −0.058 | 0.019 | −0.993 | 103,150.566 | 0.3209 | ||
| Age_End37 Weeks:annotation_by_overlapMicroglial | −0.078 | 0.041 | −0.159 | 0.003 | −1.877 | 103,244.047 | 0.06054 | . | |
| Age_End37 Weeks:annotation_by_overlapOligodendrocyte | −0.106 | 0.022 | −0.149 | −0.062 | −4.718 | 103,211.773 | <.001 | *** | |
| Age_End37 Weeks:annotation_by_overlapAstrocyte | −0.141 | 0.029 | −0.198 | −0.085 | −4.900 | 103,245.175 | <.001 | *** | |
| Age_End37 Weeks:annotation_by_overlapOPC | −0.163 | 0.045 | −0.252 | −0.075 | −3.603 | 103,242.886 | <.001 | *** | |
| Age_End37 Weeks:annotation_by_overlapExcitatory-Inhibitory | −0.230 | 0.059 | −0.346 | −0.114 | −3.883 | 103,244.313 | <.001 | *** | |
| ConditionSpace Flight:annotation_by_overlapInhibitory | 0.067 | 0.024 | 0.019 | 0.114 | 2.764 | 103,196.150 | 0.005714 | ** | |
| ConditionSpace Flight:annotation_by_overlapMicroglial | 0.340 | 0.044 | 0.253 | 0.427 | 7.663 | 103,240.892 | <.001 | *** | |
| ConditionSpace Flight:annotation_by_overlapOligodendrocyte | 0.120 | 0.025 | 0.070 | 0.170 | 4.748 | 103,243.306 | <.001 | *** | |
| ConditionSpace Flight:annotation_by_overlapAstrocyte | 0.016 | 0.035 | −0.052 | 0.083 | 0.449 | 103,243.711 | 0.6537 | ||
| ConditionSpace Flight:annotation_by_overlapOPC | 0.387 | 0.049 | 0.291 | 0.483 | 7.891 | 103,238.966 | <.001 | *** | |
| ConditionSpace Flight:annotation_by_overlapExcitatory-Inhibitory | 0.004 | 0.052 | −0.098 | 0.106 | 0.077 | 103,245.119 | 0.9386 | ||
| Age_End37 Weeks:ConditionSpace Flight:annotation_by_overlapInhibitory | −0.068 | 0.033 | −0.132 | −0.003 | −2.064 | 103,173.362 | 0.03905 | * | |
| Age_End37 Weeks:ConditionSpace Flight:annotation_by_overlapMicroglial | 0.072 | 0.065 | −0.054 | 0.199 | 1.118 | 103,243.812 | 0.2634 | ||
| Age_End37 Weeks:ConditionSpace Flight:annotation_by_overlapOligodendrocyte | −0.062 | 0.035 | −0.131 | 0.008 | −1.741 | 103,217.073 | 0.08165 | . | |
| Age_End37 Weeks:ConditionSpace Flight:annotation_by_overlapAstrocyte | 0.355 | 0.049 | 0.260 | 0.451 | 7.317 | 103,245.919 | <.001 | *** | |
| Age_End37 Weeks:ConditionSpace Flight:annotation_by_overlapOPC | 0.141 | 0.071 | 0.001 | 0.281 | 1.979 | 103,242.401 | 0.04783 | * | |
| Age_End37 Weeks:ConditionSpace Flight:annotation_by_overlapExcitatory-Inhibitory | 0.144 | 0.091 | −0.035 | 0.323 | 1.578 | 103,245.625 | 0.1146 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
| Formula: lmer(senscore~Condition*Age_End+(1|sample)+(1+Condition|annotation_by_overlap)) | |||||||||
| Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -138765 AIC: 277548 | BIC: 277634 Residual error: 0.927 |
|||||||||
| Random Effects: | Estimate | SE | CI-low | CI-high | T-stat | DF | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.156 | |||||||
| annotation_by_overlap-sd | (Intercept) | 0.494 | |||||||
| annotation_by_overlap-sd | ConditionSpace Flight | 0.176 | |||||||
| annotation_by_overlap-cor | (Intercept) | 0.652 | |||||||
| Residual-sd | Observation | 0.927 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | −0.089 | 0.199 | −0.552 | 0.374 | −0.445 | 7.711 | 0.6687 | ||
| ConditionSpace Flight | 0.283 | 0.125 | 0.023 | 0.544 | 2.274 | 19.689 | 0.03435 | * | |
| Age_End37 Weeks | 0.180 | 0.105 | −0.045 | 0.406 | 1.714 | 13.852 | 0.1088 | ||
| ConditionSpace Flight:Age_End37 Weeks | −0.077 | 0.149 | −0.396 | 0.242 | −0.517 | 13.910 | 0.6134 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
| Formula: lmer(senscore~Condition*Age_End+(1|sample)+(1+Condition+Age_End|annotation_by_overlap)) | |||||||||
| Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -138732 AIC: 277488 | BIC: 277603 Residual error: 0.926 |
|||||||||
| Random Effects: | Estimate | SE | CI-low | CI-high | T-stat | DF | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.155 | |||||||
| annotation_by_overlap-sd | (Intercept) | 0.482 | |||||||
| annotation_by_overlap-sd | ConditionSpace Flight | 0.172 | |||||||
| annotation_by_overlap-sd | Age_End37 Weeks | 0.064 | |||||||
| annotation_by_overlap-cor | (Intercept) | 0.674 | |||||||
| annotation_by_overlap-cor | (Intercept) | 0.589 | |||||||
| annotation_by_overlap-cor | ConditionSpace Flight | −0.200 | |||||||
| Residual-sd | Observation | 0.926 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | −0.081 | 0.195 | −0.533 | 0.370 | −0.418 | 7.774 | 0.6872 | ||
| ConditionSpace Flight | 0.284 | 0.123 | 0.027 | 0.542 | 2.306 | 19.655 | 0.03212 | * | |
| Age_End37 Weeks | 0.156 | 0.107 | −0.073 | 0.384 | 1.451 | 15.287 | 0.1671 | ||
| ConditionSpace Flight:Age_End37 Weeks | −0.074 | 0.148 | −0.392 | 0.243 | −0.504 | 13.890 | 0.6224 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
Binary Snc/Not¶
Multilevel Logistic Regression
In [14]:
glmmod = {}
# for x in ["1", f"1 + {col_condition}", f"1 + {col_condition} + {col_age}"]:
# fff = str(f"Senescent_Cell ~ {col_age} * {col_condition}"
# f" + (1 | {col_sample}) + ({x} | {col_celltype})")
for x in ["1", f"1 + Spaceflight", f"1 + Spaceflight + Aged"]:
sep = "|" if x == "1" else "||"
fff = str(f"Senescent_Cell ~ Aged * Spaceflight"
f" + (1 | {col_sample}) + ({x} {sep} {col_celltype})")
glmmod[x] = glmer(fff, data=r_rna, family="binomial")
# glmmod[x].set_factors(factors)
glmmod[x].fit(exponentiate=True, ncpus=ncpus) # estimates => odds
# print(glmmod[x].emmeans(col_condition))
# print(glmmod[x].emmeans(col_age))
glmmod[x].summary().show()
print(glmmod[x].ranef[col_celltype])
compare(*[glmmod[x] for x in glmmod])
# Plot
# grid = sns.FacetGrid(self.rna.obs, col=col_age, hue=col_condition)
# grid.map(sns.countplot, "Senescent_Cell", order=[False, True])
# grid.add_legend()
# grid.set_xlabels("")
# grid.set_xticklabels(["Not SnC", "Snc"])
# sns.despine()
# fig = plt.figure(figsize=(10, 2))
# g_s = fig.add_gridspec(1, 2, width_ratios=[1, 3])
# left, right = fig.add_subplot(g_s[0]), fig.add_subplot(g_s[1])
# left = sns.kdeplot(x="resid", data=glmmod.data, ax=left)
# left.set(xlabel="Residuals", title="Error Distribution")
# right.stem(glmmod.data["cooksd"], basefmt=" ", label="Cook's Distance")
# right.set(xlabel="Row Number", ylabel="Cook's Distance",
# title="Influential Observations",
# xticks=range(glmmod.data.height))
# sns.despine()
| Formula: glmer(Senescent_Cell~Aged*Spaceflight+(1|sample)+(1|annotation_by_overlap)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -8098 AIC: 16209 | BIC: 16266 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.576 | |||||||
| annotation_by_overlap-sd | (Intercept) | 0.258 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.008 | 0.005 | 0.014 | 0.002 | −18.014 | inf | <.001 | *** | |
| Aged | 1.930 | 0.984 | 3.785 | 0.663 | 1.913 | inf | 0.05575 | . | |
| Spaceflight | 2.252 | 1.131 | 4.485 | 0.792 | 2.309 | inf | 0.02095 | * | |
| Aged:Spaceflight | 0.648 | 0.266 | 1.579 | 0.294 | −0.955 | inf | 0.3395 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
shape: (7, 2) ┌───────────────────────┬─────────────┐ │ level ┆ (Intercept) │ │ --- ┆ --- │ │ str ┆ f64 │ ╞═══════════════════════╪═════════════╡ │ Excitatory ┆ 0.818122 │ │ Inhibitory ┆ 0.990964 │ │ Microglial ┆ 1.030949 │ │ Oligodendrocyte ┆ 0.92749 │ │ Astrocyte ┆ 0.709976 │ │ OPC ┆ 1.485906 │ │ Excitatory-Inhibitory ┆ 1.255562 │ └───────────────────────┴─────────────┘
| Formula: glmer(Senescent_Cell~Aged*Spaceflight+(1|sample)+(1+Spaceflight||annotation_by_overlap)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -8097 AIC: 16208 | BIC: 16275 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.578 | |||||||
| annotation_by_overlap-sd | (Intercept) | 0.199 | |||||||
| annotation_by_overlap.1-sd | Spaceflight | 0.190 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.008 | 0.005 | 0.014 | 0.002 | −16.655 | inf | <.001 | *** | |
| Aged | 1.907 | 0.832 | 4.373 | 0.808 | 1.525 | inf | 0.1274 | ||
| Spaceflight | 2.353 | 1.022 | 5.418 | 1.001 | 2.012 | inf | 0.04427 | * | |
| Aged:Spaceflight | 0.667 | 0.216 | 2.059 | 0.384 | −0.704 | inf | 0.4816 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
shape: (7, 3) ┌───────────────────────┬─────────────┬─────────────┐ │ level ┆ (Intercept) ┆ Spaceflight │ │ --- ┆ --- ┆ --- │ │ str ┆ f64 ┆ f64 │ ╞═══════════════════════╪═════════════╪═════════════╡ │ Excitatory ┆ 0.904116 ┆ 0.812104 │ │ Inhibitory ┆ 1.00822 ┆ 0.975283 │ │ Microglial ┆ 1.033172 ┆ 0.994406 │ │ Oligodendrocyte ┆ 0.91031 ┆ 1.053992 │ │ Astrocyte ┆ 0.771405 ┆ 0.873702 │ │ OPC ┆ 1.308158 ┆ 1.248044 │ │ Excitatory-Inhibitory ┆ 1.178191 ┆ 1.113457 │ └───────────────────────┴─────────────┴─────────────┘
| Formula: glmer(Senescent_Cell~Aged*Spaceflight+(1|sample)+(1+Spaceflight+Aged||annotation_by_overlap)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -8092 AIC: 16201 | BIC: 16277 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.577 | |||||||
| annotation_by_overlap-sd | (Intercept) | 0.242 | |||||||
| annotation_by_overlap.1-sd | Spaceflight | 0.183 | |||||||
| annotation_by_overlap.2-sd | Aged | 0.209 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.008 | 0.005 | 0.014 | 0.002 | −17.589 | inf | <.001 | *** | |
| Aged | 1.901 | 0.871 | 4.151 | 0.757 | 1.613 | inf | 0.1067 | ||
| Spaceflight | 2.323 | 1.144 | 4.716 | 0.839 | 2.332 | inf | 0.0197 | * | |
| Aged:Spaceflight | 0.674 | 0.245 | 1.856 | 0.348 | −0.763 | inf | 0.4454 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
shape: (7, 4) ┌───────────────────────┬─────────────┬─────────────┬──────────┐ │ level ┆ (Intercept) ┆ Spaceflight ┆ Aged │ │ --- ┆ --- ┆ --- ┆ --- │ │ str ┆ f64 ┆ f64 ┆ f64 │ ╞═══════════════════════╪═════════════╪═════════════╪══════════╡ │ Excitatory ┆ 0.771457 ┆ 0.804849 ┆ 1.278735 │ │ Inhibitory ┆ 1.07736 ┆ 0.994875 ┆ 0.875142 │ │ Microglial ┆ 0.999886 ┆ 0.989842 ┆ 1.109774 │ │ Oligodendrocyte ┆ 1.006319 ┆ 1.069577 ┆ 0.803853 │ │ Astrocyte ┆ 0.746049 ┆ 0.8915 ┆ 1.025683 │ │ OPC ┆ 1.31507 ┆ 1.21047 ┆ 1.094022 │ │ Excitatory-Inhibitory ┆ 1.254969 ┆ 1.101261 ┆ 0.900711 │ └───────────────────────┴─────────────┴─────────────┴──────────┘
Out[14]:
| Analysis of Deviance Table | |||||||||
| Model 1: glmer(Senescent_Cell~AgedSpaceflight+(1|sample)+(1|annotation_by_overlap)) Model 2: glmer(Senescent_Cell~AgedSpaceflight+(1|sample)+(1+Spaceflight||annotation_by_overlap)) Model 3: glmer(Senescent_Cell~Aged*Spaceflight+(1|sample)+(1+Spaceflight+Aged||annotation_by_overlap)) |
|||||||||
| AIC | BIC | logLik | npar | -2*log(L) | Chisq | Df | Pr(>Chisq) | ||
|---|---|---|---|---|---|---|---|---|---|
| 1.00 | 16209.7 | 16267 | −8098.9 | 6.00 | 16.20K | ||||
| 2.00 | 16208.6 | 16275.4 | −8097.3 | 7.00 | 16.19K | 3.10 | 1.00 | 0.0782 | . |
| 3.00 | 16201.2 | 16277.6 | −8092.6 | 8.00 | 16.19K | 9.42 | 1.00 | 0.00215 | ** |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
Burden (Percent SnC)¶
ANOVA: Percent SnCs across (a) Condition x Age and (b) Condition x Age x Cell Type
In [15]:
# Age + Condition
model_1 = lm(
f"Senescent_Cell_Burden ~ {col_condition} + {col_age}", data=r_dff)
model_1.set_factors(factors)
model_1.anova(summary=True).show()
model_1.summary().show()
# Age + Condition + Cell Type
model_2 = lm(
f"Senescent_Cell_Burden ~ {col_condition} + {col_age} + {col_celltype}",
data=r_dff)
model_2.set_factors(factors)
model_2.anova(summary=True).show()
model_2.summary().show()
# Age + Condition * Cell Type
model_3 = lm(
f"Senescent_Cell_Burden ~ {col_age} + {col_condition} * {col_celltype}",
data=r_dff)
model_3.set_factors(factors)
model_3.anova(summary=True).show()
# # Age * Condition * Cell Type
# model_4 = lm(
# f"Senescent_Cell_Burden ~ {col_age} * {col_condition} * {col_celltype}",
# data=r_dff)
# model_4.set_factors(factors)
# model_4.anova(summary=True).show()
# model_4.summary().show()
# Compare
# compare(model_1, model_2, model_3, model_4).show()
compare(model_1, model_2, model_3).show()
# compare(model_1, model_2).show()
| ANOVA (Type III tests) | |||||
| model term | df1 | df2 | F_ratio | p_value | |
|---|---|---|---|---|---|
| Condition | 1 | 121 | 6.324 | 0.01322 | * |
| Age_End | 1 | 121 | 0.487 | 0.4865 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||
| Formula: lm(Senescent_Cell_Burden~Condition+Age_End) | ||||||||
| Number of observations: 124 Confidence intervals: parametric --------------------- R-squared: 0.0569 R-squared-adj: 0.0413 F(2, 121) = 3.65, p = 0.0289 Log-likelihood: -224 AIC: 456 | BIC: 468 Residual error: 1.497 |
||||||||
| Estimate | SE | CI-low | CI-high | T-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 1.250 | 0.224 | 0.807 | 1.693 | 5.587 | 121 | <.001 | *** |
| ConditionSpace Flight | 0.681 | 0.271 | 0.145 | 1.216 | 2.515 | 121 | 0.01322 | * |
| Age_End37 Weeks | 0.189 | 0.271 | −0.347 | 0.725 | 0.698 | 121 | 0.4865 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||||
| ANOVA (Type III tests) | |||||
| model term | df1 | df2 | F_ratio | p_value | |
|---|---|---|---|---|---|
| Condition | 1 | 115 | 6.623 | 0.01134 | * |
| Age_End | 1 | 115 | 0.541 | 0.4634 | |
| annotation_by_overlap | 6 | 115 | 1.995 | 0.07193 | . |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||
| Formula: lm(Senescent_Cell_Burden~Condition+Age_End+annotation_by_overlap) | ||||||||
| Number of observations: 124 Confidence intervals: parametric --------------------- R-squared: 0.1458 R-squared-adj: 0.0864 F(8, 115) = 2.454, p = 0.0172 Log-likelihood: -218 AIC: 456 | BIC: 484 Residual error: 1.461 |
||||||||
| Estimate | SE | CI-low | CI-high | T-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 0.837 | 0.387 | 0.071 | 1.603 | 2.164 | 115 | 0.03253 | * |
| ConditionSpace Flight | 0.680 | 0.264 | 0.157 | 1.203 | 2.574 | 115 | 0.01134 | * |
| Age_End37 Weeks | 0.195 | 0.264 | −0.329 | 0.718 | 0.736 | 115 | 0.4634 | |
| annotation_by_overlapInhibitory | 0.447 | 0.487 | −0.518 | 1.412 | 0.918 | 115 | 0.3606 | |
| annotation_by_overlapMicroglial | 0.435 | 0.487 | −0.530 | 1.400 | 0.894 | 115 | 0.3733 | |
| annotation_by_overlapOligodendrocyte | 0.288 | 0.487 | −0.677 | 1.253 | 0.592 | 115 | 0.5553 | |
| annotation_by_overlapAstrocyte | −0.193 | 0.487 | −1.158 | 0.772 | −0.396 | 115 | 0.6928 | |
| annotation_by_overlapOPC | 1.329 | 0.487 | 0.365 | 2.294 | 2.729 | 115 | 0.007348 | ** |
| annotation_by_overlapExcitatory-Inhibitory | 0.583 | 0.502 | −0.412 | 1.578 | 1.160 | 115 | 0.2484 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||||
| ANOVA (Type III tests) | |||||
| model term | df1 | df2 | F_ratio | p_value | |
|---|---|---|---|---|---|
| Age_End | 1 | 109 | 0.537 | 0.4652 | |
| Condition | 1 | 109 | 6.274 | 0.01373 | * |
| annotation_by_overlap | 6 | 109 | 1.955 | 0.07829 | . |
| Condition:annotation_by_overlap | 6 | 109 | 0.618 | 0.7154 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||
| Analysis of Deviance Table | ||||||||||
| Model 1: lm(Senescent_Cell_Burden~Condition+Age_End) Model 2: lm(Senescent_Cell_Burden~Condition+Age_End+annotation_by_overlap) Model 3: lm(Senescent_Cell_Burden~Age_End+Condition*annotation_by_overlap) |
||||||||||
| AIC | BIC | logLik | Res_Df | RSS | Df | Sum of Sq | F | Pr(>F) | ||
|---|---|---|---|---|---|---|---|---|---|---|
| 1.00 | 456.9 | 468.2 | −224.5 | 121.00 | 271.18 | |||||
| 2.00 | 456.6 | 484.8 | −218.3 | 115.00 | 245.61 | 6.00 | 25.57 | 1.96 | 0.0783 | . |
| 3.00 | 464.5 | 509.6 | −216.2 | 109.00 | 237.53 | 6.00 | 8.08 | 0.62 | 0.715 | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||||||
SnC (Binary) by Cell Type¶
With Cell Type REs¶
In [27]:
fff = str(f"Senescent_Cell ~ Aged + Spaceflight * {col_celltype} + "
f"(1 | sample) + (1 + Spaceflight + Aged || {col_celltype})")
glmmod_ct = glmer(fff, data=r_rna, family="binomial")
glmmod_ct.fit(exponentiate=True, ncpus=ncpus) # estimates => odds
glmmod_ct.summary().show()
R messages:
boundary (singular) fit: see help('isSingular')
R messages:
boundary (singular) fit: see help('isSingular')
R messages:
boundary (singular) fit: see help('isSingular')
| Formula: glmer(Senescent_Cell~Aged+Spaceflight*annotation_by_overlap+(1|sample)+(1+Spaceflight+Aged||annotation_by_overlap)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -8079 AIC: 16197 | BIC: 16378 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.578 | |||||||
| annotation_by_overlap-sd | (Intercept) | 0.000 | |||||||
| annotation_by_overlap.1-sd | Spaceflight | 0.000 | |||||||
| annotation_by_overlap.2-sd | Aged | 0.205 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.007 | 0.004 | 0.011 | 0.002 | −21.812 | inf | <.001 | *** | |
| Aged | 1.551 | 0.910 | 2.645 | 0.422 | 1.613 | inf | 0.1068 | ||
| Spaceflight | 1.460 | 0.843 | 2.528 | 0.409 | 1.350 | inf | 0.177 | ||
| annotation_by_overlapInhibitory | 1.421 | 1.118 | 1.806 | 0.174 | 2.868 | inf | 0.004129 | ** | |
| annotation_by_overlapMicroglial | 1.322 | 0.883 | 1.977 | 0.272 | 1.357 | inf | 0.1748 | ||
| annotation_by_overlapOligodendrocyte | 1.294 | 0.977 | 1.715 | 0.186 | 1.796 | inf | 0.07245 | . | |
| annotation_by_overlapAstrocyte | 0.917 | 0.656 | 1.281 | 0.156 | −0.507 | inf | 0.6122 | ||
| annotation_by_overlapOPC | 1.723 | 1.135 | 2.617 | 0.367 | 2.553 | inf | 0.01068 | * | |
| annotation_by_overlapExcitatory-Inhibitory | 1.754 | 1.077 | 2.854 | 0.436 | 2.260 | inf | 0.02385 | * | |
| Spaceflight:annotation_by_overlapInhibitory | 1.289 | 0.989 | 1.680 | 0.174 | 1.880 | inf | 0.06014 | . | |
| Spaceflight:annotation_by_overlapMicroglial | 1.267 | 0.770 | 2.083 | 0.322 | 0.932 | inf | 0.3515 | ||
| Spaceflight:annotation_by_overlapOligodendrocyte | 1.441 | 1.066 | 1.949 | 0.222 | 2.373 | inf | 0.01765 | * | |
| Spaceflight:annotation_by_overlapAstrocyte | 1.084 | 0.693 | 1.696 | 0.247 | 0.355 | inf | 0.7228 | ||
| Spaceflight:annotation_by_overlapOPC | 1.863 | 1.161 | 2.988 | 0.449 | 2.580 | inf | 0.009885 | ** | |
| Spaceflight:annotation_by_overlapExcitatory-Inhibitory | 1.566 | 0.865 | 2.834 | 0.474 | 1.482 | inf | 0.1385 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
Without Cell Type REs¶
In [251]:
fff = str(f"Senescent_Cell ~ Aged + Spaceflight * {col_celltype} + "
"(1 | sample)")
glmmod_ct2 = glmer(fff, data=r_rna, family="binomial")
glmmod_ct2.fit(exponentiate=True, ncpus=ncpus) # estimates => odds
glmmod_ct2.summary().show()
| Formula: glmer(Senescent_Cell~Aged+Spaceflight*annotation_by_overlap+(1|sample)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -8083 AIC: 16199 | BIC: 16352 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.581 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.008 | 0.005 | 0.013 | 0.002 | −19.803 | inf | <.001 | *** | |
| Aged | 1.563 | 0.896 | 2.727 | 0.444 | 1.572 | inf | 0.116 | ||
| Spaceflight | 1.491 | 0.846 | 2.625 | 0.431 | 1.382 | inf | 0.167 | ||
| annotation_by_overlapInhibitory | 1.105 | 0.925 | 1.321 | 0.100 | 1.097 | inf | 0.2725 | ||
| annotation_by_overlapMicroglial | 1.165 | 0.807 | 1.683 | 0.219 | 0.816 | inf | 0.4143 | ||
| annotation_by_overlapOligodendrocyte | 0.963 | 0.777 | 1.192 | 0.105 | −0.349 | inf | 0.7275 | ||
| annotation_by_overlapAstrocyte | 0.799 | 0.596 | 1.072 | 0.120 | −1.498 | inf | 0.1342 | ||
| annotation_by_overlapOPC | 1.459 | 1.014 | 2.098 | 0.271 | 2.037 | inf | 0.04167 | * | |
| annotation_by_overlapExcitatory-Inhibitory | 1.371 | 0.852 | 2.207 | 0.333 | 1.299 | inf | 0.1941 | ||
| Spaceflight:annotation_by_overlapInhibitory | 1.245 | 0.954 | 1.625 | 0.169 | 1.613 | inf | 0.1068 | ||
| Spaceflight:annotation_by_overlapMicroglial | 1.235 | 0.731 | 2.085 | 0.330 | 0.788 | inf | 0.4305 | ||
| Spaceflight:annotation_by_overlapOligodendrocyte | 1.417 | 1.046 | 1.918 | 0.219 | 2.250 | inf | 0.02445 | * | |
| Spaceflight:annotation_by_overlapAstrocyte | 1.069 | 0.671 | 1.702 | 0.254 | 0.280 | inf | 0.7794 | ||
| Spaceflight:annotation_by_overlapOPC | 1.822 | 1.121 | 2.962 | 0.452 | 2.422 | inf | 0.01544 | * | |
| Spaceflight:annotation_by_overlapExcitatory-Inhibitory | 1.562 | 0.804 | 3.036 | 0.530 | 1.317 | inf | 0.188 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
Just Age as Predictor¶
In [38]:
model_cac_mlm_i3_a = lmer(
f"senscore ~ {col_age} * {col_celltype} + (1 | {col_sample})",
data=r_rna) # setup model
model_cac_mlm_i3_a.set_factors(factors)
model_cac_mlm_i3_a.set_transforms({"senscore": "zscore"})
model_cac_mlm_i3_a.fit()
model_cac_mlm_i3_a.summary().show()
fff = str(f"Senescent_Cell ~ Aged * {col_celltype} + "
"(1 | sample)")
glmmod_ct2a = glmer(fff, data=r_rna, family="binomial")
glmmod_ct2a.fit(exponentiate=True, ncpus=ncpus) # estimates => odds
glmmod_ct2a.summary().show()
| Formula: lmer(senscore~Age_End*annotation_by_overlap+(1|sample)) | |||||||||
| Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -138881 AIC: 277795 | BIC: 277947 Residual error: 0.928 |
|||||||||
| Random Effects: | Estimate | SE | CI-low | CI-high | T-stat | DF | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.166 | |||||||
| Residual-sd | Observation | 0.928 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | −0.014 | 0.056 | −0.132 | 0.104 | −0.244 | 16.336 | 0.81 | ||
| Age_End37 Weeks | 0.199 | 0.079 | 0.032 | 0.365 | 2.519 | 16.267 | 0.0226 | * | |
| annotation_by_overlapInhibitory | −0.223 | 0.011 | −0.245 | −0.201 | −19.724 | 103,257.788 | <.001 | *** | |
| annotation_by_overlapMicroglial | 0.861 | 0.022 | 0.818 | 0.903 | 39.821 | 103,249.264 | *** | ||
| annotation_by_overlapOligodendrocyte | −0.409 | 0.012 | −0.432 | −0.385 | −33.734 | 103,258.553 | <.001 | *** | |
| annotation_by_overlapAstrocyte | 0.498 | 0.016 | 0.467 | 0.529 | 31.269 | 103,253.186 | <.001 | *** | |
| annotation_by_overlapOPC | 0.274 | 0.024 | 0.228 | 0.321 | 11.560 | 103,248.370 | <.001 | *** | |
| annotation_by_overlapExcitatory-Inhibitory | −0.631 | 0.025 | −0.680 | −0.583 | −25.543 | 103,253.754 | <.001 | *** | |
| Age_End37 Weeks:annotation_by_overlapInhibitory | −0.040 | 0.016 | −0.070 | −0.009 | −2.525 | 103,223.181 | 0.01158 | * | |
| Age_End37 Weeks:annotation_by_overlapMicroglial | −0.033 | 0.032 | −0.095 | 0.030 | −1.027 | 103,255.054 | 0.3042 | ||
| Age_End37 Weeks:annotation_by_overlapOligodendrocyte | −0.126 | 0.017 | −0.160 | −0.092 | −7.286 | 103,249.497 | <.001 | *** | |
| Age_End37 Weeks:annotation_by_overlapAstrocyte | −0.000 | 0.023 | −0.045 | 0.045 | −0.003 | 103,259.812 | 0.9978 | ||
| Age_End37 Weeks:annotation_by_overlapOPC | −0.077 | 0.035 | −0.145 | −0.008 | −2.194 | 103,253.684 | 0.02826 | * | |
| Age_End37 Weeks:annotation_by_overlapExcitatory-Inhibitory | −0.172 | 0.045 | −0.260 | −0.085 | −3.847 | 103,257.305 | <.001 | *** | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
| Formula: glmer(Senescent_Cell~Aged*annotation_by_overlap+(1|sample)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -8078 AIC: 16187 | BIC: 16331 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.644 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.008 | 0.005 | 0.012 | 0.002 | −21.425 | inf | <.001 | *** | |
| Aged | 2.315 | 1.255 | 4.272 | 0.724 | 2.687 | inf | 0.007214 | ** | |
| annotation_by_overlapInhibitory | 1.687 | 1.353 | 2.103 | 0.190 | 4.653 | inf | <.001 | *** | |
| annotation_by_overlapMicroglial | 1.410 | 0.929 | 2.142 | 0.301 | 1.612 | inf | 0.1069 | ||
| annotation_by_overlapOligodendrocyte | 1.678 | 1.320 | 2.132 | 0.205 | 4.228 | inf | <.001 | *** | |
| annotation_by_overlapAstrocyte | 0.951 | 0.662 | 1.367 | 0.176 | −0.270 | inf | 0.7869 | ||
| annotation_by_overlapOPC | 2.452 | 1.680 | 3.579 | 0.473 | 4.650 | inf | <.001 | *** | |
| annotation_by_overlapExcitatory-Inhibitory | 2.595 | 1.782 | 3.780 | 0.498 | 4.972 | inf | <.001 | *** | |
| Aged:annotation_by_overlapInhibitory | 0.610 | 0.463 | 0.804 | 0.086 | −3.511 | inf | <.001 | *** | |
| Aged:annotation_by_overlapMicroglial | 0.930 | 0.539 | 1.604 | 0.259 | −0.262 | inf | 0.7934 | ||
| Aged:annotation_by_overlapOligodendrocyte | 0.532 | 0.388 | 0.729 | 0.086 | −3.922 | inf | <.001 | *** | |
| Aged:annotation_by_overlapAstrocyte | 0.852 | 0.532 | 1.364 | 0.205 | −0.666 | inf | 0.5053 | ||
| Aged:annotation_by_overlapOPC | 0.755 | 0.459 | 1.241 | 0.191 | −1.109 | inf | 0.2675 | ||
| Aged:annotation_by_overlapExcitatory-Inhibitory | 0.403 | 0.207 | 0.784 | 0.137 | −2.675 | inf | 0.007467 | ** | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
Three-Way Interaction¶
In [15]:
# Score
# Binary
fff = str(f"senscore ~ {col_celltype} * Aged * Spaceflight"
f" + (1 | {col_sample})")
model_allix_score = lmer(fff, data=r_rna)
model_allix_score.fit(ncpus=ncpus) # estimates => odds
model_allix_score.anova(summary=True).show()
model_allix_score.summary().show()
# Binary
fff = str(f"Senescent_Cell ~ {col_celltype} * Aged * Spaceflight"
f" + (1 | {col_sample})")
model_allix = glmer(fff, data=r_rna, family="binomial")
model_allix.fit(exponentiate=True, ncpus=ncpus) # estimates => odds
model_allix.anova(summary=True).show()
model_allix.summary().show()
res_allix_fixed = pd.DataFrame(model_allix.result_fit[[
"term", "estimate", "p_value"]], columns=[
"Term", "OR", "P"]).set_index("Term")
res_allix_fixed.loc[:, "Log-Odds"] = np.log(res_allix_fixed.OR.astype(float))
res_allix_fixed.loc[:, "Significance"] = res_allix_fixed.P.apply(
lambda x: "***" if x < 0.001 else "**" if x < 0.01 else "*" if (
x < 0.05) else "")
res_allix_fixed
R messages: Note: D.f. calculations have been disabled because the number of observations exceeds 3000. To enable adjustments, add the argument 'lmerTest.limit = 103274' (or larger) [or, globally, 'set emm_options(lmerTest.limit = 103274)' or larger]; but be warned that this may result in large computation time and memory use. R messages: Note: D.f. calculations have been disabled because the number of observations exceeds 3000. To enable adjustments, add the argument 'lmerTest.limit = 103274' (or larger) [or, globally, 'set emm_options(lmerTest.limit = 103274)' or larger]; but be warned that this may result in large computation time and memory use.
| ANOVA (Type III tests) | ||||||
| model term | df1 | df2 | F_ratio | Chisq | p_value | |
|---|---|---|---|---|---|---|
| annotation_by_overlap | 6.000 | inf | 2,007.061 | 12,042.366 | *** | |
| Aged | 1.000 | inf | 2.781 | 2.781 | 0.09538 | . |
| Spaceflight | 1.000 | inf | 11.624 | 11.624 | <.001 | *** |
| annotation_by_overlap:Aged | 6.000 | inf | 13.785 | 82.710 | <.001 | *** |
| annotation_by_overlap:Spaceflight | 6.000 | inf | 52.540 | 315.240 | <.001 | *** |
| Aged:Spaceflight | 1.000 | inf | 0.000 | 0.000 | 0.9975 | |
| annotation_by_overlap:Aged:Spaceflight | 6.000 | inf | 14.504 | 87.024 | <.001 | *** |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||
| Formula: lmer(senscore~annotation_by_overlap*Aged*Spaceflight+(1|sample)) | |||||||||
| Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: 138206 AIC: -276352 | BIC: -276066 Residual error: 0.063 |
|||||||||
| Random Effects: | Estimate | SE | CI-low | CI-high | T-stat | DF | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.011 | |||||||
| Residual-sd | Observation | 0.063 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | −0.027 | 0.005 | −0.037 | −0.016 | −5.532 | 14.224 | <.001 | *** | |
| annotation_by_overlapInhibitory | −0.017 | 0.001 | −0.019 | −0.015 | −17.907 | 103,243.694 | <.001 | *** | |
| annotation_by_overlapMicroglial | 0.050 | 0.002 | 0.046 | 0.054 | 26.502 | 103,233.445 | <.001 | *** | |
| annotation_by_overlapOligodendrocyte | −0.031 | 0.001 | −0.033 | −0.029 | −29.905 | 103,239.753 | <.001 | *** | |
| annotation_by_overlapAstrocyte | 0.034 | 0.001 | 0.031 | 0.036 | 25.775 | 103,235.512 | <.001 | *** | |
| annotation_by_overlapOPC | 0.009 | 0.002 | 0.005 | 0.013 | 4.444 | 103,234.599 | <.001 | *** | |
| annotation_by_overlapExcitatory-Inhibitory | −0.043 | 0.002 | −0.047 | −0.039 | −20.904 | 103,237.636 | <.001 | *** | |
| Aged | 0.016 | 0.007 | 0.000 | 0.031 | 2.178 | 14.129 | 0.04687 | * | |
| Spaceflight | 0.008 | 0.007 | −0.007 | 0.024 | 1.144 | 14.375 | 0.2713 | ||
| annotation_by_overlapInhibitory:Aged | −0.001 | 0.001 | −0.004 | 0.001 | −0.993 | 103,150.561 | 0.3209 | ||
| annotation_by_overlapMicroglial:Aged | −0.005 | 0.003 | −0.011 | 0.000 | −1.877 | 103,244.047 | 0.06054 | . | |
| annotation_by_overlapOligodendrocyte:Aged | −0.007 | 0.002 | −0.010 | −0.004 | −4.718 | 103,211.772 | <.001 | *** | |
| annotation_by_overlapAstrocyte:Aged | −0.010 | 0.002 | −0.014 | −0.006 | −4.900 | 103,245.175 | <.001 | *** | |
| annotation_by_overlapOPC:Aged | −0.011 | 0.003 | −0.017 | −0.005 | −3.603 | 103,242.886 | <.001 | *** | |
| annotation_by_overlapExcitatory-Inhibitory:Aged | −0.016 | 0.004 | −0.024 | −0.008 | −3.883 | 103,244.313 | <.001 | *** | |
| annotation_by_overlapInhibitory:Spaceflight | 0.005 | 0.002 | 0.001 | 0.008 | 2.764 | 103,196.150 | 0.005714 | ** | |
| annotation_by_overlapMicroglial:Spaceflight | 0.023 | 0.003 | 0.017 | 0.029 | 7.663 | 103,240.892 | <.001 | *** | |
| annotation_by_overlapOligodendrocyte:Spaceflight | 0.008 | 0.002 | 0.005 | 0.012 | 4.748 | 103,243.306 | <.001 | *** | |
| annotation_by_overlapAstrocyte:Spaceflight | 0.001 | 0.002 | −0.004 | 0.006 | 0.449 | 103,243.711 | 0.6537 | ||
| annotation_by_overlapOPC:Spaceflight | 0.026 | 0.003 | 0.020 | 0.033 | 7.891 | 103,238.966 | <.001 | *** | |
| annotation_by_overlapExcitatory-Inhibitory:Spaceflight | 0.000 | 0.004 | −0.007 | 0.007 | 0.077 | 103,245.119 | 0.9386 | ||
| Aged:Spaceflight | −0.006 | 0.010 | −0.028 | 0.016 | −0.554 | 14.273 | 0.5879 | ||
| annotation_by_overlapInhibitory:Aged:Spaceflight | −0.005 | 0.002 | −0.009 | −0.000 | −2.064 | 103,173.360 | 0.03905 | * | |
| annotation_by_overlapMicroglial:Aged:Spaceflight | 0.005 | 0.004 | −0.004 | 0.014 | 1.118 | 103,243.812 | 0.2634 | ||
| annotation_by_overlapOligodendrocyte:Aged:Spaceflight | −0.004 | 0.002 | −0.009 | 0.001 | −1.741 | 103,217.072 | 0.08165 | . | |
| annotation_by_overlapAstrocyte:Aged:Spaceflight | 0.024 | 0.003 | 0.018 | 0.031 | 7.317 | 103,245.919 | <.001 | *** | |
| annotation_by_overlapOPC:Aged:Spaceflight | 0.010 | 0.005 | 0.000 | 0.019 | 1.979 | 103,242.401 | 0.04783 | * | |
| annotation_by_overlapExcitatory-Inhibitory:Aged:Spaceflight | 0.010 | 0.006 | −0.002 | 0.022 | 1.578 | 103,245.625 | 0.1146 | ||
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
R messages: Convergence status : [1] FALSE attr(,"gradient") [1] 0.001041441 R messages: Convergence status : [1] FALSE attr(,"gradient") [1] 0.001041441
| ANOVA (Type III tests) | ||||||
| model term | df1 | df2 | F_ratio | Chisq | p_value | |
|---|---|---|---|---|---|---|
| annotation_by_overlap | 6.000 | inf | 9.367 | 56.202 | <.001 | *** |
| Aged | 1.000 | inf | 2.623 | 2.623 | 0.1053 | |
| Spaceflight | 1.000 | inf | 4.065 | 4.065 | 0.04378 | * |
| annotation_by_overlap:Aged | 6.000 | inf | 5.508 | 33.048 | <.001 | *** |
| annotation_by_overlap:Spaceflight | 6.000 | inf | 3.346 | 20.076 | 0.002681 | ** |
| Aged:Spaceflight | 1.000 | inf | 0.330 | 0.330 | 0.5654 | |
| annotation_by_overlap:Aged:Spaceflight | 6.000 | inf | 7.459 | 44.754 | <.001 | *** |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | ||||||
| Formula: glmer(Senescent_Cell~annotation_by_overlap*Aged*Spaceflight+(1|sample)) | |||||||||
| Family: binomial (link: default) Number of observations: 103274 Confidence intervals: parametric --------------------- Log-likelihood: -8046 AIC: 16151 | BIC: 16428 Residual error: 1.0 |
|||||||||
| Random Effects: | Estimate | CI-low | CI-high | SE | Z-stat | df | p | ||
|---|---|---|---|---|---|---|---|---|---|
| sample-sd | (Intercept) | 0.566 | |||||||
| Fixed Effects: | |||||||||
| (Intercept) | 0.007 | 0.004 | 0.012 | 0.002 | −18.077 | inf | <.001 | *** | |
| annotation_by_overlapInhibitory | 0.990 | 0.745 | 1.315 | 0.143 | −0.072 | inf | 0.9423 | ||
| annotation_by_overlapMicroglial | 1.057 | 0.593 | 1.883 | 0.312 | 0.187 | inf | 0.8517 | ||
| annotation_by_overlapOligodendrocyte | 1.184 | 0.864 | 1.622 | 0.190 | 1.051 | inf | 0.2932 | ||
| annotation_by_overlapAstrocyte | 0.979 | 0.659 | 1.453 | 0.197 | −0.107 | inf | 0.9151 | ||
| annotation_by_overlapOPC | 1.185 | 0.619 | 2.271 | 0.393 | 0.513 | inf | 0.6081 | ||
| annotation_by_overlapExcitatory-Inhibitory | 1.487 | 0.816 | 2.709 | 0.455 | 1.296 | inf | 0.1948 | ||
| Aged | 1.937 | 0.873 | 4.295 | 0.787 | 1.627 | inf | 0.1037 | ||
| Spaceflight | 0.912 | 0.384 | 2.163 | 0.402 | −0.210 | inf | 0.8338 | ||
| annotation_by_overlapInhibitory:Aged | 1.227 | 0.852 | 1.768 | 0.228 | 1.101 | inf | 0.2709 | ||
| annotation_by_overlapMicroglial:Aged | 1.207 | 0.563 | 2.589 | 0.470 | 0.484 | inf | 0.6284 | ||
| annotation_by_overlapOligodendrocyte:Aged | 0.674 | 0.437 | 1.041 | 0.149 | −1.780 | inf | 0.07506 | . | |
| annotation_by_overlapAstrocyte:Aged | 0.645 | 0.354 | 1.177 | 0.198 | −1.429 | inf | 0.153 | ||
| annotation_by_overlapOPC:Aged | 1.426 | 0.623 | 3.260 | 0.602 | 0.840 | inf | 0.4008 | ||
| annotation_by_overlapExcitatory-Inhibitory:Aged | 0.842 | 0.342 | 2.073 | 0.387 | −0.374 | inf | 0.7082 | ||
| annotation_by_overlapInhibitory:Spaceflight | 3.990 | 2.388 | 6.667 | 1.045 | 5.282 | inf | <.001 | *** | |
| annotation_by_overlapMicroglial:Spaceflight | 2.383 | 0.973 | 5.838 | 1.089 | 1.900 | inf | 0.05746 | . | |
| annotation_by_overlapOligodendrocyte:Spaceflight | 2.660 | 1.539 | 4.596 | 0.742 | 3.505 | inf | <.001 | *** | |
| annotation_by_overlapAstrocyte:Spaceflight | 0.705 | 0.258 | 1.925 | 0.361 | −0.681 | inf | 0.4958 | ||
| annotation_by_overlapOPC:Spaceflight | 4.920 | 2.078 | 11.647 | 2.163 | 3.623 | inf | <.001 | *** | |
| annotation_by_overlapExcitatory-Inhibitory:Spaceflight | 3.770 | 1.576 | 9.017 | 1.677 | 2.983 | inf | 0.002858 | ** | |
| Aged:Spaceflight | 1.634 | 0.502 | 5.321 | 0.984 | 0.816 | inf | 0.4146 | ||
| annotation_by_overlapInhibitory:Aged:Spaceflight | 0.174 | 0.093 | 0.323 | 0.055 | −5.524 | inf | <.001 | *** | |
| annotation_by_overlapMicroglial:Aged:Spaceflight | 0.446 | 0.142 | 1.407 | 0.261 | −1.377 | inf | 0.1685 | ||
| annotation_by_overlapOligodendrocyte:Aged:Spaceflight | 0.467 | 0.234 | 0.932 | 0.165 | −2.160 | inf | 0.03075 | * | |
| annotation_by_overlapAstrocyte:Aged:Spaceflight | 2.310 | 0.706 | 7.558 | 1.397 | 1.384 | inf | 0.1663 | ||
| annotation_by_overlapOPC:Aged:Spaceflight | 0.245 | 0.082 | 0.735 | 0.137 | −2.508 | inf | 0.01213 | * | |
| annotation_by_overlapExcitatory-Inhibitory:Aged:Spaceflight | 0.155 | 0.029 | 0.818 | 0.132 | −2.196 | inf | 0.02811 | * | |
| Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 | |||||||||
Out[15]:
| OR | P | Log-Odds | Significance | |
|---|---|---|---|---|
| Term | ||||
| (Intercept) | 0.007257 | 0.0 | -4.925800 | *** |
| annotation_by_overlapInhibitory | 0.989572 | 0.942323 | -0.010483 | |
| annotation_by_overlapMicroglial | 1.056651 | 0.851745 | 0.055105 | |
| annotation_by_overlapOligodendrocyte | 1.1839 | 0.293218 | 0.168814 | |
| annotation_by_overlapAstrocyte | 0.97873 | 0.915072 | -0.021499 | |
| annotation_by_overlapOPC | 1.185386 | 0.608123 | 0.170068 | |
| annotation_by_overlapExcitatory-Inhibitory | 1.486958 | 0.194837 | 0.396733 | |
| Aged | 1.936852 | 0.103733 | 0.661064 | |
| Spaceflight | 0.911654 | 0.833786 | -0.092494 | |
| annotation_by_overlapInhibitory:Aged | 1.22744 | 0.270895 | 0.204931 | |
| annotation_by_overlapMicroglial:Aged | 1.207347 | 0.628373 | 0.188426 | |
| annotation_by_overlapOligodendrocyte:Aged | 0.674097 | 0.075064 | -0.394381 | |
| annotation_by_overlapAstrocyte:Aged | 0.645351 | 0.152988 | -0.437960 | |
| annotation_by_overlapOPC:Aged | 1.425591 | 0.400752 | 0.354586 | |
| annotation_by_overlapExcitatory-Inhibitory:Aged | 0.84194 | 0.708198 | -0.172047 | |
| annotation_by_overlapInhibitory:Spaceflight | 3.98989 | 0.0 | 1.383764 | *** |
| annotation_by_overlapMicroglial:Spaceflight | 2.383131 | 0.057459 | 0.868415 | |
| annotation_by_overlapOligodendrocyte:Spaceflight | 2.659605 | 0.000457 | 0.978178 | *** |
| annotation_by_overlapAstrocyte:Spaceflight | 0.705464 | 0.49579 | -0.348899 | |
| annotation_by_overlapOPC:Spaceflight | 4.919576 | 0.000291 | 1.593222 | *** |
| annotation_by_overlapExcitatory-Inhibitory:Spaceflight | 3.76993 | 0.002858 | 1.327056 | ** |
| Aged:Spaceflight | 1.634474 | 0.414581 | 0.491321 | |
| annotation_by_overlapInhibitory:Aged:Spaceflight | 0.173642 | 0.0 | -1.750761 | *** |
| annotation_by_overlapMicroglial:Aged:Spaceflight | 0.446422 | 0.168462 | -0.806491 | |
| annotation_by_overlapOligodendrocyte:Aged:Spaceflight | 0.466986 | 0.030751 | -0.761456 | * |
| annotation_by_overlapAstrocyte:Aged:Spaceflight | 2.309757 | 0.166317 | 0.837142 | |
| annotation_by_overlapOPC:Aged:Spaceflight | 0.244922 | 0.012127 | -1.406815 | * |
| annotation_by_overlapExcitatory-Inhibitory:Aged:Spaceflight | 0.154854 | 0.028111 | -1.865273 | * |
Perturbation Distance¶
In [16]:
print(f"\n\n{'=' * 80}\nOverall Distance\n{'=' * 80}\n\n")
df_distance, figs_distance = scflow.ax.analyze_perturbation_distance(
self.rna, col_condition=[col_age, col_condition, col_batch])
print(f"\n\n{'=' * 80}\nJust Senescence Genes\n{'=' * 80}\n\n")
df_distance_snc, figs_distance_snc = scflow.ax.analyze_perturbation_distance(
self.rna[:, genes], col_condition=[
col_age, col_condition, col_batch])
Output()
================================================================================ Overall Distance ================================================================================
Output()
Output()
================================================================================ Just Senescence Genes ================================================================================
Output()
Output()
Output()
Output()
Composition Analysis¶
Cell Type¶
In [ ]:
print(f"\n\n{'=' * 80}\nOverall Sample\n{'=' * 80}\n\n")
out_ctc = scflow.ax.analyze_composition(
self.rna, col_celltype, col_batch, col_sample=col_sample,
palette=palette[col_batch],
formula=None, key_modality="coda", reference_cell_type="automatic",
absence_threshold=0.1, est_fdr=0.1, level_order=[keys[
col_batch]["key_control"]] + keys[col_batch]["key_treatment"])
plt.show()
print(f"\n\n{'=' * 80}\nAged Subset\n{'=' * 80}\n\n")
out_ctc_sf = scflow.ax.analyze_composition(
self.rna[self.rna.obs[col_age] != keys[col_age]["key_control"]],
col_celltype, col_condition, col_sample=col_sample,
formula=None, key_modality="coda", reference_cell_type="automatic",
absence_threshold=0.1, est_fdr=0.1, level_order=[keys[col_condition][
"key_control"]] + [keys[col_condition]["key_treatment"]])
Senescent Cells¶
Tree-Aggregated¶
In [11]:
# col_celltype_hierarchy = [
# "Senescent_Cell_Label", f"SnC_hierarchy",
# "Senescent_Cell_Label_by_Type"]
col_celltype_hierarchy = [
f"{col_celltype}_hierarchical", "SnC_hierarchy",
"Senescent_Cell_Label_by_Type"]
ref_celltype = self.rna.obs["Senescent_Cell_Label_by_Type"].value_counts(
).index.values[0]
form_c = " + ".join([col_condition, col_age]) + " + " + " * ".join([
col_condition, col_age])
By Just SF¶
In [18]:
_ = scflow.ax.analyze_composition_tree(
self.rna, col_celltype_hierarchy[-1], col_condition,
col_sample=col_sample, formula=None, seed=0, est_fdr=0.2,
reference_cell_type=ref_celltype, num_warmup=2000,
col_celltype_hierarchy=col_celltype_hierarchy)
# if overwrite is True or os.path.exists(
# f"outputs/tasccoda_results.pkl") is False:
# print(f"Writing to outputs/tasccoda_results.pkl")
# with open(f"outputs/tasccoda_results.pkl", "wb") as f:
# pickle.dump(out_ctc_ct_tree[0], f)
# # out_ctc_ct_tree[1].write_h5mu(f"outputs/tasccoda_data.h5mu")
<string>:20: DeprecationWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, pd.CategoricalDtype) instead
• Zero counts encountered in data! Added a pseudocount of 0.5.
sample: 100%|█| 12000/12000 [01:11<00:00, 168.59it/s, 15 steps of size 2.22e-01
Compositional Analysis summary ┌──────────────────────────────────┬──────────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├──────────────────────────────────┼──────────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 14 cell types │ │ Reference cell type │ Excitatory │ │ Formula │ Condition │ │ Reference index │ [8, 2, 0] │ │ MCMC Sampling │ Sampled 10000 chain states (2000 burnin samples) │ │ Acceptance rate │ 89.7% │ └──────────────────────────────────┴──────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Astrocyte 1.674 1.363 1.966 0.161 484.005 │ │ Excitatory 3.066 2.848 3.309 0.123 1947.097 │ │ Excitatory-Inhibitory 0.060 -0.344 0.450 0.214 96.360 │ │ Inhibitory 2.789 2.540 3.067 0.139 1476.005 │ │ Microglial 1.028 0.720 1.332 0.165 253.685 │ │ OPC 0.830 0.487 1.145 0.177 208.116 │ │ Oligodendrocyte 2.446 2.174 2.694 0.139 1047.430 │ │ SnC Astrocyte -1.258 -1.714 -0.788 0.248 25.793 │ │ SnC Excitatory -0.951 -1.394 -0.494 0.239 35.061 │ │ SnC Excitatory-Inhibitory -1.499 -1.968 -1.032 0.252 20.269 │ │ SnC Inhibitory -0.595 -1.022 -0.159 0.231 50.054 │ │ SnC Microglial -1.298 -1.766 -0.850 0.245 24.781 │ │ SnC OPC -1.252 -1.712 -0.801 0.245 25.948 │ │ SnC Oligodendrocyte -0.739 -1.198 -0.338 0.230 43.341 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Effect Expected Sample log2-fold change │ │ Covariate Cell Type │ │ T.Space Flight Astrocyte 0.000 484.005 0.000 │ │ Excitatory 0.000 1947.097 0.000 │ │ Excitatory-Inhibitory 0.000 96.360 0.000 │ │ Inhibitory 0.000 1476.005 0.000 │ │ Microglial 0.000 253.685 0.000 │ │ OPC 0.000 208.116 0.000 │ │ Oligodendrocyte 0.000 1047.430 0.000 │ │ SnC Astrocyte 0.000 25.793 0.000 │ │ SnC Excitatory 0.000 35.061 0.000 │ │ SnC Excitatory-Inhibitory 0.000 20.269 0.000 │ │ SnC Inhibitory 0.000 50.054 0.000 │ │ SnC Microglial 0.000 24.781 0.000 │ │ SnC OPC 0.000 25.948 0.000 │ │ SnC Oligodendrocyte 0.000 43.341 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Median HDI 3% HDI 97% SD │ │ Covariate Cell Type │ │ T.Space Flight Astrocyte 0.036 -0.217 0.240 0.118 │ │ Excitatory 0.000 0.000 0.000 0.000 │ │ Excitatory-Inhibitory 0.040 -0.134 0.254 0.099 │ │ Inhibitory -0.001 -0.229 0.136 0.100 │ │ Microglial 0.041 -0.114 0.246 0.089 │ │ OPC 0.065 -0.140 0.286 0.111 │ │ Oligodendrocyte 0.079 -0.091 0.274 0.095 │ │ SnC Astrocyte 0.139 -0.145 0.467 0.159 │ │ SnC Excitatory 0.151 -0.134 0.464 0.158 │ │ SnC Excitatory-Inhibitory 0.142 -0.139 0.462 0.159 │ │ SnC Inhibitory 0.148 -0.117 0.475 0.158 │ │ SnC Microglial 0.096 -0.136 0.358 0.128 │ │ SnC OPC 0.150 -0.113 0.490 0.159 │ │ SnC Oligodendrocyte 0.151 -0.122 0.472 0.158 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Nodes │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Covariate=Condition[T.Space Flight]_node │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Median HDI 3% HDI 97% SD Delta Is credible │ │ Node │ │ Non-Senescent 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Senescent 0.00 0.05 -0.08 0.23 0.08 0.13 False │ │ Neuron 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Macroglia 0.00 0.03 -0.10 0.17 0.07 0.13 False │ │ Microglial 0.00 0.04 -0.11 0.25 0.09 0.13 False │ │ SnC Neuron 0.00 0.04 -0.11 0.23 0.09 0.13 False │ │ SnC Macroglia 0.00 0.04 -0.12 0.23 0.09 0.13 False │ │ SnC Microglial 0.00 0.04 -0.17 0.22 0.10 0.13 False │ │ Excitatory 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Excitatory-Inhibitory 0.00 0.04 -0.13 0.25 0.10 0.13 False │ │ Inhibitory 0.00 -0.00 -0.23 0.14 0.10 0.13 False │ │ Oligodendrocyte 0.00 0.05 -0.08 0.21 0.07 0.13 False │ │ OPC 0.00 0.04 -0.12 0.23 0.09 0.13 False │ │ Astrocyte 0.00 0.02 -0.22 0.16 0.10 0.13 False │ │ SnC Excitatory 0.00 0.04 -0.16 0.24 0.10 0.13 False │ │ SnC Inhibitory 0.00 0.04 -0.14 0.26 0.10 0.13 False │ │ SnC Excitatory-Inhibitory 0.00 0.04 -0.15 0.25 0.10 0.13 False │ │ SnC Oligodendrocyte 0.00 0.04 -0.13 0.26 0.10 0.13 False │ │ SnC Astrocyte 0.00 0.03 -0.18 0.22 0.10 0.13 False │ │ SnC OPC 0.00 0.04 -0.14 0.26 0.10 0.13 False │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
Wrote file: /home/easlinger/AWG/ADBR/Brain_AWG/Senescence/tree_effect.png
<Figure size 0x300 with 0 Axes>
By SF & Age¶
In [12]:
_ = scflow.ax.analyze_composition_tree(
self.rna, "Senescent_Cell_Label_by_Type", [col_condition, col_age],
formula=form_c, col_sample=col_sample, seed=0,
reference_cell_type=ref_celltype, est_fdr=0.2,
col_celltype_hierarchy=col_celltype_hierarchy)
# if overwrite is True or os.path.exists(
# f"outputs/tasccoda_results_ca.pkl") is False:
# with open(f"outputs/tasccoda_results_ca.pkl", "wb") as f:
# pickle.dump(out_ctc_ct_tree[0], f)
# # out_ctc_ct_tree[1].write_h5mu(f"outputs/tasccoda_data_ca.h5mu")
/home/easlinger/scflow/scflow/analysis/perturbation.py:190: DeprecationWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, pd.CategoricalDtype) instead if pd.api.types.is_categorical_dtype(adata.obs[i]):
• Zero counts encountered in data! Added a pseudocount of 0.5.
INFO:2025-08-21 19:16:12,616:jax._src.xla_bridge:830: Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory 2025-08-21 19:16:12 | [INFO] Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory sample: 100%|█| 11000/11000 [02:01<00:00, 90.63it/s, 31 steps of size 1.35e-01. 2025-08-21 19:18:20 | [INFO] arviz_base not installed 2025-08-21 19:18:20 | [INFO] arviz_stats not installed 2025-08-21 19:18:20 | [INFO] arviz_plots not installed
Compositional Analysis summary ┌──────────────────────────────────┬──────────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├──────────────────────────────────┼──────────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 14 cell types │ │ Reference cell type │ Excitatory │ │ Formula │ Condition + Age_End + Condition * Age_End │ │ Reference index │ [6, 0] │ │ MCMC Sampling │ Sampled 10000 chain states (1000 burnin samples) │ │ Acceptance rate │ 92.6% │ └──────────────────────────────────┴──────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Astrocyte 1.971 1.605 2.324 0.191 526.950 │ │ Excitatory 3.187 2.933 3.430 0.132 1777.752 │ │ Excitatory-Inhibitory 0.549 -0.093 1.181 0.354 127.117 │ │ Inhibitory 3.013 2.685 3.331 0.172 1493.839 │ │ Microglial 1.178 0.819 1.540 0.190 238.437 │ │ OPC 1.107 0.729 1.508 0.208 222.095 │ │ Oligodendrocyte 2.755 2.437 3.080 0.172 1154.133 │ │ SnC Astrocyte -1.179 -1.665 -0.709 0.254 22.581 │ │ SnC Excitatory -0.878 -1.330 -0.420 0.246 30.512 │ │ SnC Excitatory-Inhibitory -1.424 -1.910 -0.935 0.261 17.674 │ │ SnC Inhibitory -0.498 -0.927 -0.050 0.233 44.617 │ │ SnC Microglial -1.233 -1.736 -0.787 0.253 21.394 │ │ SnC OPC -1.180 -1.637 -0.689 0.253 22.558 │ │ SnC Oligodendrocyte -0.651 -1.124 -0.222 0.242 38.287 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Effect Expected Sample log2-fold change │ │ Covariate Cell Type │ │ T.Space Flight Astrocyte 0.000 526.950 0.000 │ │ Excitatory 0.000 1777.752 0.000 │ │ Excitatory-Inhibitory 0.000 127.117 0.000 │ │ Inhibitory 0.000 1493.839 0.000 │ │ Microglial 0.000 238.437 0.000 │ │ OPC 0.000 222.095 0.000 │ │ Oligodendrocyte 0.000 1154.133 0.000 │ │ SnC Astrocyte 0.000 22.581 0.000 │ │ SnC Excitatory 0.000 30.512 0.000 │ │ SnC Excitatory-Inhibitory 0.000 17.674 0.000 │ │ SnC Inhibitory 0.000 44.617 0.000 │ │ SnC Microglial 0.000 21.394 0.000 │ │ SnC OPC 0.000 22.558 0.000 │ │ SnC Oligodendrocyte 0.000 38.287 0.000 │ │ Age_EndT.37 Weeks Astrocyte -0.295 433.344 -0.282 │ │ Excitatory 0.000 1963.595 0.143 │ │ Excitatory-Inhibitory -0.593 77.597 -0.712 │ │ Inhibitory 0.000 1650.003 0.143 │ │ Microglial 0.000 263.363 0.143 │ │ OPC -0.295 182.643 -0.282 │ │ Oligodendrocyte -0.295 949.117 -0.282 │ │ SnC Astrocyte 0.000 24.941 0.143 │ │ SnC Excitatory 0.000 33.701 0.143 │ │ SnC Excitatory-Inhibitory 0.000 19.522 0.143 │ │ SnC Inhibitory 0.000 49.281 0.143 │ │ SnC Microglial 0.000 23.630 0.143 │ │ SnC OPC 0.000 24.917 0.143 │ │ SnC Oligodendrocyte 0.000 42.289 0.143 │ │ T.Space Flight:Age_EndT.37 Weeks Astrocyte 0.000 526.950 0.000 │ │ Excitatory 0.000 1777.752 0.000 │ │ Excitatory-Inhibitory 0.000 127.117 0.000 │ │ Inhibitory 0.000 1493.839 0.000 │ │ Microglial 0.000 238.437 0.000 │ │ OPC 0.000 222.095 0.000 │ │ Oligodendrocyte 0.000 1154.133 0.000 │ │ SnC Astrocyte 0.000 22.581 0.000 │ │ SnC Excitatory 0.000 30.512 0.000 │ │ SnC Excitatory-Inhibitory 0.000 17.674 0.000 │ │ SnC Inhibitory 0.000 44.617 0.000 │ │ SnC Microglial 0.000 21.394 0.000 │ │ SnC OPC 0.000 22.558 0.000 │ │ SnC Oligodendrocyte 0.000 38.287 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Median HDI 3% HDI 97% SD │ │ Covariate Cell Type │ │ T.Space Flight Astrocyte -0.000 -0.240 0.166 0.105 │ │ Excitatory 0.000 0.000 0.000 0.000 │ │ Excitatory-Inhibitory 0.011 -0.172 0.201 0.093 │ │ Inhibitory -0.020 -0.240 0.089 0.092 │ │ Microglial 0.011 -0.144 0.194 0.084 │ │ OPC 0.022 -0.179 0.227 0.103 │ │ Oligodendrocyte 0.039 -0.119 0.227 0.089 │ │ SnC Astrocyte 0.030 -0.191 0.331 0.133 │ │ SnC Excitatory 0.038 -0.200 0.327 0.135 │ │ SnC Excitatory-Inhibitory 0.031 -0.195 0.324 0.132 │ │ SnC Inhibitory 0.034 -0.189 0.342 0.133 │ │ SnC Microglial 0.010 -0.178 0.222 0.100 │ │ SnC OPC 0.036 -0.184 0.328 0.132 │ │ SnC Oligodendrocyte 0.037 -0.186 0.337 0.135 │ │ Age_EndT.37 Weeks Astrocyte -0.302 -0.605 0.030 0.174 │ │ Excitatory 0.000 0.000 0.000 0.000 │ │ Excitatory-Inhibitory -0.593 -1.354 0.065 0.454 │ │ Inhibitory -0.124 -0.442 0.048 0.148 │ │ Microglial -0.012 -0.322 0.134 0.128 │ │ OPC -0.293 -0.625 0.040 0.182 │ │ Oligodendrocyte -0.334 -0.633 -0.028 0.160 │ │ SnC Astrocyte 0.012 -0.239 0.261 0.127 │ │ SnC Excitatory 0.023 -0.217 0.281 0.126 │ │ SnC Excitatory-Inhibitory 0.011 -0.261 0.248 0.129 │ │ SnC Inhibitory 0.017 -0.231 0.265 0.124 │ │ SnC Microglial 0.008 -0.174 0.224 0.100 │ │ SnC OPC 0.012 -0.257 0.248 0.128 │ │ SnC Oligodendrocyte 0.008 -0.261 0.232 0.126 │ │ T.Space Flight:Age_EndT.37 Weeks Astrocyte -0.005 -0.269 0.183 0.118 │ │ Excitatory 0.000 0.000 0.000 0.000 │ │ Excitatory-Inhibitory 0.004 -0.223 0.196 0.107 │ │ Inhibitory -0.013 -0.288 0.104 0.108 │ │ Microglial 0.003 -0.204 0.161 0.096 │ │ OPC 0.003 -0.259 0.210 0.119 │ │ Oligodendrocyte 0.001 -0.229 0.189 0.106 │ │ SnC Astrocyte 0.024 -0.236 0.290 0.132 │ │ SnC Excitatory 0.021 -0.250 0.292 0.134 │ │ SnC Excitatory-Inhibitory 0.013 -0.258 0.266 0.132 │ │ SnC Inhibitory 0.015 -0.248 0.267 0.131 │ │ SnC Microglial 0.010 -0.162 0.237 0.099 │ │ SnC OPC 0.024 -0.241 0.284 0.131 │ │ SnC Oligodendrocyte 0.021 -0.225 0.297 0.132 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Nodes │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Covariate=Age_End[T.37 Weeks]_node │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Median HDI 3% HDI 97% SD Delta Is credible │ │ Node │ │ Neuron 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Macroglia -0.29 -0.29 -0.58 0.02 0.17 0.16 True │ │ SnC Neuron 0.00 0.01 -0.15 0.18 0.08 0.16 False │ │ SnC Macroglia 0.00 0.01 -0.19 0.16 0.08 0.16 False │ │ Microglial 0.00 -0.01 -0.32 0.13 0.13 0.16 False │ │ SnC Microglial 0.00 0.01 -0.17 0.22 0.10 0.16 False │ │ Excitatory 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Excitatory-Inhibitory -0.59 -0.59 -1.35 0.07 0.45 0.16 True │ │ Inhibitory 0.00 -0.12 -0.44 0.05 0.15 0.16 False │ │ Oligodendrocyte 0.00 -0.01 -0.30 0.12 0.11 0.16 False │ │ OPC 0.00 0.01 -0.18 0.17 0.09 0.16 False │ │ Astrocyte 0.00 0.00 -0.21 0.14 0.09 0.16 False │ │ SnC Excitatory 0.00 0.01 -0.17 0.22 0.10 0.16 False │ │ SnC Inhibitory 0.00 0.01 -0.17 0.21 0.10 0.16 False │ │ SnC Excitatory-Inhibitory 0.00 0.01 -0.21 0.20 0.10 0.16 False │ │ SnC Oligodendrocyte 0.00 0.01 -0.19 0.19 0.10 0.16 False │ │ SnC Astrocyte 0.00 0.01 -0.18 0.21 0.10 0.16 False │ │ SnC OPC 0.00 0.01 -0.20 0.19 0.10 0.16 False │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Covariate=Condition[T.Space Flight]:Age_End[T.37 Weeks]_node │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Median HDI 3% HDI 97% SD Delta Is credible │ │ Node │ │ Neuron 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Macroglia 0.00 0.00 -0.19 0.14 0.08 0.16 False │ │ SnC Neuron 0.00 0.01 -0.17 0.18 0.09 0.16 False │ │ SnC Macroglia 0.00 0.01 -0.15 0.19 0.09 0.16 False │ │ Microglial 0.00 0.00 -0.20 0.16 0.10 0.16 False │ │ SnC Microglial 0.00 0.01 -0.16 0.24 0.10 0.16 False │ │ Excitatory 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Excitatory-Inhibitory 0.00 0.00 -0.22 0.20 0.11 0.16 False │ │ Inhibitory 0.00 -0.01 -0.29 0.10 0.11 0.16 False │ │ Oligodendrocyte 0.00 0.01 -0.16 0.15 0.08 0.16 False │ │ OPC 0.00 0.01 -0.19 0.17 0.09 0.16 False │ │ Astrocyte 0.00 0.00 -0.20 0.14 0.09 0.16 False │ │ SnC Excitatory 0.00 0.01 -0.18 0.24 0.10 0.16 False │ │ SnC Inhibitory 0.00 0.01 -0.20 0.19 0.10 0.16 False │ │ SnC Excitatory-Inhibitory 0.00 0.01 -0.23 0.17 0.10 0.16 False │ │ SnC Oligodendrocyte 0.00 0.01 -0.20 0.20 0.10 0.16 False │ │ SnC Astrocyte 0.00 0.01 -0.17 0.22 0.10 0.16 False │ │ SnC OPC 0.00 0.01 -0.18 0.22 0.10 0.16 False │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Covariate=Condition[T.Space Flight]_node │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Median HDI 3% HDI 97% SD Delta Is credible │ │ Node │ │ Neuron 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Macroglia 0.00 0.01 -0.11 0.14 0.06 0.16 False │ │ SnC Neuron 0.00 0.01 -0.13 0.23 0.09 0.16 False │ │ SnC Macroglia 0.00 0.01 -0.13 0.22 0.09 0.16 False │ │ Microglial 0.00 0.01 -0.14 0.19 0.08 0.16 False │ │ SnC Microglial 0.00 0.01 -0.18 0.22 0.10 0.16 False │ │ Excitatory 0.00 0.00 0.00 0.00 0.00 0.00 False │ │ Excitatory-Inhibitory 0.00 0.01 -0.17 0.20 0.09 0.16 False │ │ Inhibitory 0.00 -0.02 -0.24 0.09 0.09 0.16 False │ │ Oligodendrocyte 0.00 0.02 -0.09 0.21 0.08 0.16 False │ │ OPC 0.00 0.01 -0.15 0.19 0.08 0.16 False │ │ Astrocyte 0.00 -0.00 -0.23 0.12 0.09 0.16 False │ │ SnC Excitatory 0.00 0.01 -0.15 0.25 0.10 0.16 False │ │ SnC Inhibitory 0.00 0.01 -0.18 0.22 0.10 0.16 False │ │ SnC Excitatory-Inhibitory 0.00 0.01 -0.18 0.22 0.10 0.16 False │ │ SnC Oligodendrocyte 0.00 0.01 -0.15 0.25 0.10 0.16 False │ │ SnC Astrocyte 0.00 0.01 -0.18 0.21 0.10 0.16 False │ │ SnC OPC 0.00 0.01 -0.17 0.22 0.10 0.16 False │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pertpy/tools/_coda/_base_coda.py:1990: PerformanceWarning: indexing past lexsort depth may impact performance. node_effs = data.uns["scCODA_params"]["node_df"].loc[(covariate + "_node",),].copy() /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pertpy/tools/_coda/_base_coda.py:2000: PerformanceWarning: indexing past lexsort depth may impact performance. leaf_effs = eff_df.loc[(covariate,),].copy() /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pertpy/tools/_coda/_base_coda.py:1990: PerformanceWarning: indexing past lexsort depth may impact performance. node_effs = data.uns["scCODA_params"]["node_df"].loc[(covariate + "_node",),].copy() /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pertpy/tools/_coda/_base_coda.py:2000: PerformanceWarning: indexing past lexsort depth may impact performance. leaf_effs = eff_df.loc[(covariate,),].copy() /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pertpy/tools/_coda/_base_coda.py:1990: PerformanceWarning: indexing past lexsort depth may impact performance. node_effs = data.uns["scCODA_params"]["node_df"].loc[(covariate + "_node",),].copy() /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/pertpy/tools/_coda/_base_coda.py:2000: PerformanceWarning: indexing past lexsort depth may impact performance. leaf_effs = eff_df.loc[(covariate,),].copy()
Wrote file: /home/easlinger/AWG/ADBR/Brain_AWG/Senescence/tree_effect.png Wrote file: /home/easlinger/AWG/ADBR/Brain_AWG/Senescence/tree_effect.png Wrote file: /home/easlinger/AWG/ADBR/Brain_AWG/Senescence/tree_effect.png
scCoda¶
In [ ]:
# print(f"\n\n{'=' * 80}\nOverall Sample\n{'=' * 80}\n\n")
# out_ctc = scflow.ax.analyze_composition(
# self.rna, "Senescent_Cell_Label", col_batch, col_sample=col_sample,
# formula=None, key_modality="coda", reference_cell_type="Non-Senescent",
# palette=palette[col_batch],
# absence_threshold=0.1, est_fdr=0.1, seed=1618, level_order=[keys[
# col_batch]["key_control"]] + keys[col_batch]["key_treatment"])
# plt.show()
# print(f"\n\n{'=' * 80}\nAged Subset\n{'=' * 80}\n\n")
# out_ctc_old = scflow.ax.analyze_composition(
# self.rna[self.rna.obs[col_age] == keys[col_age]["key_treatment"]],
# "Senescent_Cell_Label", col_condition, col_sample=col_sample,
# palette=palette[col_batch],
# formula=None, key_modality="coda", reference_cell_type="Non-Senescent",
# absence_threshold=0.1, est_fdr=0.1, level_order=[keys[
# col_condition]["key_control"]] + [
# keys[col_condition]["key_treatment"]])
print(f"\n\n{'=' * 80}\nBy Cell Type\n{'=' * 80}\n\n")
out_ctc_ct = {}
for x in self.rna.obs[col_celltype].unique():
out_ctc_ct[x] = scflow.ax.analyze_composition(
self.rna[self.rna.obs[col_celltype] == x],
"Senescent_Cell_Label_by_Type", col_batch, col_sample=col_sample,
formula=None, key_modality="coda", full_hmc=False,
palette=palette[col_batch], seed=1618,
num_samples=10000, num_warmup=1000,
reference_cell_type=x, absence_threshold=0.1, est_fdr=0.1,
level_order=[keys[col_batch]["key_control"]] + keys[
col_batch]["key_treatment"])
if overwrite is True or os.path.exists(
f"outputs/scccoda_results_{x}.pkl") is False:
with open(f"outputs/sccoda_results_{x}.pkl", "wb") as f:
pickle.dump(out_ctc_ct[x][0], f)
out_ctc_ct[x][1].write_h5mu(f"outputs/sccoda_data_{x}.h5mu")
plt.show()
# print(f"\n\n{'=' * 80}\nBy Cell Type (Aged Subset)\n{'=' * 80}\n\n")
# ref_cell = self.rna.obs[col_celltype].value_counts().index.values[0]
# out_ctc = scflow.ax.analyze_composition(
# self.rna[self.rna.obs[col_age] == keys[col_age]["key_treatment"]],
# "Senescent_Cell_Label_by_Type", col_condition,
# palette=palette[col_batch],
# col_sample=col_sample, formula=None, absence_threshold=0.1, est_fdr=0.1,
# level_order=[keys[col_condition]["key_control"]] + [
# keys[col_condition]["key_treatment"]])
================================================================================
By Cell Type
================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
INFO:2025-08-21 13:59:15,704:jax._src.xla_bridge:830: Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory 2025-08-21 13:59:15 | [INFO] Unable to initialize backend 'tpu': INTERNAL: Failed to open libtpu.so: libtpu.so: cannot open shared object file: No such file or directory sample: 100%|█| 11000/11000 [37:07<00:00, 4.94it/s, 1023 steps of size 1.27e-1 2025-08-21 14:36:27 | [INFO] arviz_base not installed 2025-08-21 14:36:27 | [INFO] arviz_stats not installed 2025-08-21 14:36:27 | [INFO] arviz_plots not installed
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Excitatory │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (1000 burnin samples) │ │ Acceptance rate │ 94.1% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Excitatory 37.428 37.428 37.428 0.000 2291.889 │ │ SnC Excitatory -0.037 -0.037 -0.037 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks Excitatory 0.000 0.000 -98.087 │ │ SnC Excitatory 105.454 2291.889 54.051 │ │ GroupT.Space Flight | 12 Weeks Excitatory 0.000 2291.889 -0.000 │ │ SnC Excitatory 0.130 0.000 0.187 │ │ GroupT.Space Flight | 29 Weeks Excitatory 0.000 2291.889 -0.000 │ │ SnC Excitatory 0.066 0.000 0.095 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks Excitatory 0.000 0.000 0.000 0.000 │ │ SnC Excitatory 105.446 105.446 0.000 1.000 │ │ GroupT.Space Flight | 12 Weeks Excitatory 0.000 0.000 0.000 0.000 │ │ SnC Excitatory 0.130 0.130 0.000 1.000 │ │ GroupT.Space Flight | 29 Weeks Excitatory 0.000 0.000 0.000 0.000 │ │ SnC Excitatory 0.066 0.066 0.000 1.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
... storing 'rna:Senescent_Cell_Label_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label_senmayo_3' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type' as categorical ... storing 'rna:scCODA_sample_id' as categorical ... storing 'Senescent_Cell_Label_senmayo' as categorical ... storing 'Senescent_Cell_Label_senmayo_3' as categorical ... storing 'Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'Senescent_Cell_Label' as categorical ... storing 'Senescent_Cell_Label_by_Type' as categorical ... storing 'scCODA_sample_id' as categorical
================================================== Credible Effects ==================================================
Covariate Group[T.Ground Control | 29 Weeks] Group[T.Space Flight | 12 Weeks] Group[T.Space Flight | 29 Weeks]
Cell Type
Excitatory
SnC Excitatory * * *
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
sample: 100%|█| 11000/11000 [00:28<00:00, 391.91it/s, 1 steps of size 9.03e-02.
! Acceptance rate unusually low (0.04095195565575892 < 0.5)! Results might be incorrect! Please check feasibility of results and re-run the sampling step with a different rng_key if necessary.
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 16 samples, 2 cell types │ │ Reference cell type │ Excitatory-Inhibitory │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 0.811 │ │ Spike-and-slab threshold │ 0.811 │ │ MCMC Sampling │ Sampled 10000 chain states (1000 burnin samples) │ │ Acceptance rate │ 4.1% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Excitatory-Inhibitory 40.143 41.364 41.559 6.830 138.969 │ │ SnC Excitatory-Inhibitory 4.865 1.035 5.745 1.587 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks Excitatory-Inhibitory 0.000 0.000 -58.602 │ │ SnC Excitatory-Inhibitory 75.898 138.969 50.895 │ │ GroupT.Space Flight | 12 Weeks Excitatory-Inhibitory 0.000 0.000 -29.594 │ │ SnC Excitatory-Inhibitory 55.791 138.969 50.895 │ │ GroupT.Space Flight | 29 Weeks Excitatory-Inhibitory 0.000 138.969 0.000 │ │ SnC Excitatory-Inhibitory 0.003 0.000 0.004 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks Excitatory-Inhibitory 0.000 0.000 0.000 0.000 │ │ SnC Excitatory-Inhibitory -0.398 301.956 107.510 0.982 │ │ GroupT.Space Flight | 12 Weeks Excitatory-Inhibitory 0.000 0.000 0.000 0.000 │ │ SnC Excitatory-Inhibitory 35.524 135.211 33.874 0.981 │ │ GroupT.Space Flight | 29 Weeks Excitatory-Inhibitory 0.000 0.000 0.000 0.000 │ │ SnC Excitatory-Inhibitory -0.000 0.010 0.041 0.811 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
... storing 'rna:Senescent_Cell_Label_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label_senmayo_3' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type' as categorical ... storing 'rna:scCODA_sample_id' as categorical ... storing 'Senescent_Cell_Label_senmayo' as categorical ... storing 'Senescent_Cell_Label_senmayo_3' as categorical ... storing 'Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'Senescent_Cell_Label' as categorical ... storing 'Senescent_Cell_Label_by_Type' as categorical ... storing 'scCODA_sample_id' as categorical
================================================== Credible Effects ================================================== Covariate Group[T.Ground Control | 29 Weeks] Group[T.Space Flight | 12 Weeks] Group[T.Space Flight | 29 Weeks] Cell Type Excitatory-Inhibitory SnC Excitatory-Inhibitory * * * ==========================================================================================================================
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
sample: 100%|█| 11000/11000 [04:13<00:00, 43.43it/s, 127 steps of size 2.59e-02
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Oligodendrocyte │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (1000 burnin samples) │ │ Acceptance rate │ 66.7% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Oligodendrocyte 6.636 5.287 8.193 0.947 1014.390 │ │ SnC Oligodendrocyte 2.244 0.995 3.570 0.843 12.554 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks Oligodendrocyte 0.000 1014.390 0.000 │ │ SnC Oligodendrocyte 0.000 12.554 0.000 │ │ GroupT.Space Flight | 12 Weeks Oligodendrocyte 0.000 1014.390 0.000 │ │ SnC Oligodendrocyte 0.000 12.554 0.000 │ │ GroupT.Space Flight | 29 Weeks Oligodendrocyte 0.000 1014.390 0.000 │ │ SnC Oligodendrocyte 0.000 12.554 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks Oligodendrocyte 0.000 0.000 0.000 0.000 │ │ SnC Oligodendrocyte -0.501 0.363 0.122 0.296 │ │ GroupT.Space Flight | 12 Weeks Oligodendrocyte 0.000 0.000 0.000 0.000 │ │ SnC Oligodendrocyte -0.003 1.092 0.379 0.776 │ │ GroupT.Space Flight | 29 Weeks Oligodendrocyte 0.000 0.000 0.000 0.000 │ │ SnC Oligodendrocyte -0.050 0.765 0.335 0.662 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
... storing 'rna:Senescent_Cell_Label_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label_senmayo_3' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type' as categorical ... storing 'rna:scCODA_sample_id' as categorical ... storing 'Senescent_Cell_Label_senmayo' as categorical ... storing 'Senescent_Cell_Label_senmayo_3' as categorical ... storing 'Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'Senescent_Cell_Label' as categorical ... storing 'Senescent_Cell_Label_by_Type' as categorical ... storing 'scCODA_sample_id' as categorical
================================================== Credible Effects ================================================== Covariate Group[T.Ground Control | 29 Weeks] Group[T.Space Flight | 12 Weeks] Group[T.Space Flight | 29 Weeks] Cell Type Oligodendrocyte SnC Oligodendrocyte ==========================================================================================================================
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
sample: 100%|█| 11000/11000 [01:19<00:00, 138.12it/s, 1 steps of size 4.38e-02.
! Acceptance rate unusually low (0.22878863250827636 < 0.5)! Results might be incorrect! Please check feasibility of results and re-run the sampling step with a different rng_key if necessary.
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Inhibitory │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 0.928 │ │ Spike-and-slab threshold │ 0.928 │ │ MCMC Sampling │ Sampled 10000 chain states (1000 burnin samples) │ │ Acceptance rate │ 22.9% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Inhibitory 29.571 4.550 42.162 17.466 1454.611 │ │ SnC Inhibitory 3.139 0.488 5.382 2.040 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks Inhibitory 0.000 1454.611 0.000 │ │ SnC Inhibitory 0.000 0.000 0.000 │ │ GroupT.Space Flight | 12 Weeks Inhibitory 0.000 0.000 -314.641 │ │ SnC Inhibitory 244.525 1454.611 38.133 │ │ GroupT.Space Flight | 29 Weeks Inhibitory 0.000 1454.611 0.000 │ │ SnC Inhibitory 0.000 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks Inhibitory 0.000 0.000 0.000 0.000 │ │ SnC Inhibitory -0.201 322.862 136.410 0.842 │ │ GroupT.Space Flight | 12 Weeks Inhibitory 0.000 0.000 0.000 0.000 │ │ SnC Inhibitory -0.816 540.602 167.773 0.929 │ │ GroupT.Space Flight | 29 Weeks Inhibitory 0.000 0.000 0.000 0.000 │ │ SnC Inhibitory -0.692 382.915 134.219 0.781 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
... storing 'rna:Senescent_Cell_Label_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label_senmayo_3' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type' as categorical ... storing 'rna:scCODA_sample_id' as categorical ... storing 'Senescent_Cell_Label_senmayo' as categorical ... storing 'Senescent_Cell_Label_senmayo_3' as categorical ... storing 'Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'Senescent_Cell_Label' as categorical ... storing 'Senescent_Cell_Label_by_Type' as categorical ... storing 'scCODA_sample_id' as categorical
================================================== Credible Effects ==================================================
Covariate Group[T.Ground Control | 29 Weeks] Group[T.Space Flight | 12 Weeks] Group[T.Space Flight | 29 Weeks]
Cell Type
Inhibitory
SnC Inhibitory *
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
sample: 100%|█| 11000/11000 [39:03<00:00, 4.69it/s, 1023 steps of size 7.21e-1
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Microglial │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (1000 burnin samples) │ │ Acceptance rate │ 88.1% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Microglial 32.635 32.635 32.635 0.000 213.639 │ │ SnC Microglial 1.613 1.613 1.613 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks Microglial 0.000 0.000 -21.314 │ │ SnC Microglial 45.796 213.639 44.755 │ │ GroupT.Space Flight | 12 Weeks Microglial 0.000 213.639 0.000 │ │ SnC Microglial 0.000 0.000 0.000 │ │ GroupT.Space Flight | 29 Weeks Microglial 0.000 213.639 0.000 │ │ SnC Microglial 0.000 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks Microglial 0.000 0.000 0.000 0.000 │ │ SnC Microglial 45.796 45.796 0.000 1.000 │ │ GroupT.Space Flight | 12 Weeks Microglial 0.000 0.000 0.000 0.000 │ │ SnC Microglial NaN NaN 0.000 0.000 │ │ GroupT.Space Flight | 29 Weeks Microglial 0.000 0.000 0.000 0.000 │ │ SnC Microglial NaN NaN 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
... storing 'rna:Senescent_Cell_Label_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label_senmayo_3' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type' as categorical ... storing 'rna:scCODA_sample_id' as categorical ... storing 'Senescent_Cell_Label_senmayo' as categorical ... storing 'Senescent_Cell_Label_senmayo_3' as categorical ... storing 'Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'Senescent_Cell_Label' as categorical ... storing 'Senescent_Cell_Label_by_Type' as categorical ... storing 'scCODA_sample_id' as categorical
================================================== Credible Effects ==================================================
Covariate Group[T.Ground Control | 29 Weeks] Group[T.Space Flight | 12 Weeks] Group[T.Space Flight | 29 Weeks]
Cell Type
Microglial
SnC Microglial *
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
sample: 100%|█| 11000/11000 [39:54<00:00, 4.59it/s, 1023 steps of size 6.59e-1
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ OPC │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 1.000 │ │ Spike-and-slab threshold │ 1.000 │ │ MCMC Sampling │ Sampled 10000 chain states (1000 burnin samples) │ │ Acceptance rate │ 91.3% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ OPC 34.149 34.149 34.149 0.000 173.417 │ │ SnC OPC 1.289 1.289 1.289 0.000 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks OPC 0.000 0.013 -13.712 │ │ SnC OPC 42.364 173.404 47.407 │ │ GroupT.Space Flight | 12 Weeks OPC 0.000 173.417 0.000 │ │ SnC OPC 0.000 0.000 0.000 │ │ GroupT.Space Flight | 29 Weeks OPC 0.000 166.647 -0.057 │ │ SnC OPC 29.657 6.770 42.728 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks OPC 0.000 0.000 0.000 0.000 │ │ SnC OPC 42.364 42.364 0.000 1.000 │ │ GroupT.Space Flight | 12 Weeks OPC 0.000 0.000 0.000 0.000 │ │ SnC OPC NaN NaN 0.000 0.000 │ │ GroupT.Space Flight | 29 Weeks OPC 0.000 0.000 0.000 0.000 │ │ SnC OPC 29.657 29.657 0.000 1.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
... storing 'rna:Senescent_Cell_Label_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label_senmayo_3' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type' as categorical ... storing 'rna:scCODA_sample_id' as categorical ... storing 'Senescent_Cell_Label_senmayo' as categorical ... storing 'Senescent_Cell_Label_senmayo_3' as categorical ... storing 'Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'Senescent_Cell_Label' as categorical ... storing 'Senescent_Cell_Label_by_Type' as categorical ... storing 'scCODA_sample_id' as categorical
================================================== Credible Effects ==================================================
Covariate Group[T.Ground Control | 29 Weeks] Group[T.Space Flight | 12 Weeks] Group[T.Space Flight | 29 Weeks]
Cell Type
OPC
SnC OPC * *
==========================================================================================================================
• Zero counts encountered in data! Added a pseudocount of 0.5.
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
sample: 100%|█| 11000/11000 [00:45<00:00, 239.95it/s, 1 steps of size 6.86e-02.
! Acceptance rate unusually low (0.061323104336006136 < 0.5)! Results might be incorrect! Please check feasibility of results and re-run the sampling step with a different rng_key if necessary.
Compositional Analysis summary ┌───────────────────────────────────────┬─────────────────────────────────────────────────────────────────────────┐ │ Name │ Value │ ├───────────────────────────────────────┼─────────────────────────────────────────────────────────────────────────┤ │ Data │ Data: 18 samples, 2 cell types │ │ Reference cell type │ Astrocyte │ │ Formula │ Group │ │ Reference index │ 0 │ │ Spike-and-slab threshold │ 0.952 │ │ Spike-and-slab threshold │ 0.952 │ │ MCMC Sampling │ Sampled 10000 chain states (1000 burnin samples) │ │ Acceptance rate │ 6.1% │ └───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Intercepts │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter HDI 3% HDI 97% SD Expected Sample │ │ Senescent_Cell_Label_by_Type │ │ Astrocyte 32.481 4.565 42.691 16.743 453.806 │ │ SnC Astrocyte -0.299 -0.633 0.539 0.406 0.000 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ Final Parameter Expected Sample log2-fold change │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks Astrocyte 0.000 0.000 -187.327 │ │ SnC Astrocyte 162.625 453.806 47.292 │ │ GroupT.Space Flight | 12 Weeks Astrocyte 0.000 453.806 0.000 │ │ SnC Astrocyte 0.000 0.000 0.000 │ │ GroupT.Space Flight | 29 Weeks Astrocyte 0.000 0.000 -38.897 │ │ SnC Astrocyte 59.741 453.806 47.292 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐ │ Effects Extended │ ├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤ │ HDI 3% HDI 97% SD Inclusion probability │ │ Covariate Cell Type │ │ GroupT.Ground Control | 29 Weeks Astrocyte 0.000 0.000 0.000 0.000 │ │ SnC Astrocyte 40.134 242.308 76.373 0.952 │ │ GroupT.Space Flight | 12 Weeks Astrocyte 0.000 0.000 0.000 0.000 │ │ SnC Astrocyte -1.167 0.249 0.114 0.043 │ │ GroupT.Space Flight | 29 Weeks Astrocyte 0.000 0.000 0.000 0.000 │ │ SnC Astrocyte 1.583 94.959 35.984 0.975 │ └─────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
... storing 'rna:Senescent_Cell_Label_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label_senmayo_3' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'rna:Senescent_Cell_Label' as categorical ... storing 'rna:Senescent_Cell_Label_by_Type' as categorical ... storing 'rna:scCODA_sample_id' as categorical ... storing 'Senescent_Cell_Label_senmayo' as categorical ... storing 'Senescent_Cell_Label_senmayo_3' as categorical ... storing 'Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'Senescent_Cell_Label' as categorical ... storing 'Senescent_Cell_Label_by_Type' as categorical ... storing 'scCODA_sample_id' as categorical
================================================== Credible Effects ================================================== Covariate Group[T.Ground Control | 29 Weeks] Group[T.Space Flight | 12 Weeks] Group[T.Space Flight | 29 Weeks] Cell Type Astrocyte SnC Astrocyte * * ==========================================================================================================================
SnCs (Ignore Age)¶
In [ ]:
print(f"\n\n{'=' * 80}\nBy Cell Type\n{'=' * 80}\n\n")
out_ctc_ct_ageag = {}
for x in self.rna.obs[col_celltype].unique():
out_ctc_ct_ageag[x] = scflow.ax.analyze_composition(
self.rna[self.rna.obs[col_celltype] == x],
"Senescent_Cell_Label_by_Type", col_condition, col_sample=col_sample,
palette=palette[col_condition],
reference_cell_type=x, absence_threshold=0.1, est_fdr=0.1,
level_order=[keys[col_batch]["key_control"]] + keys[
col_batch]["key_treatment"])
plt.show()
Differential Gene Expression¶
Mainly using edgeR
Age¶
In older spaceflight mice compared to younger spaceflight mice
- 9630013a20Rik (CNS myelination and neural tissue regeneration) is downregulated.
- mt-Td is upregulated.
In [29]:
%matplotlib inline
# Overall
print(f"\n\n{'=' * 80}\nOverall Sample\n{'=' * 80}\n\n")
out_edgr_age = scflow.ax.run_deg_edgr(
self.rna, col_age, col_covariate=None, formula=None,
**keys[col_age], log2fc_thresh=0, n_top_vars=25,
col_celltype=col_celltype, col_sample=col_sample,
fig_title="Age DEGs: Overall Sample\n\n")
# Ground Control Subset
print(f"\n\n{'=' * 80}\nGround Control Subset\n{'=' * 80}\n\n")
out_edgr_age_gc = scflow.ax.run_deg_edgr(
self.rna[self.rna.obs[col_condition] == keys[
col_condition]["key_control"]],
col_age, col_covariate=None, formula=None,
**keys[col_age], log2fc_thresh=0, n_top_vars=25,
col_celltype=col_celltype, col_sample=col_sample,
fig_title="Age DEGs: Just Ground Control\n\n")
# Space Flight Subset
print(f"\n\n{'=' * 80}\nSpace Flight Subset\n{'=' * 80}\n\n")
out_edgr_age_sf = scflow.ax.run_deg_edgr(
self.rna[self.rna.obs[col_condition] == keys[
col_condition]["key_treatment"]],
col_age, col_covariate=None, formula=None, **keys[col_age],
log2fc_thresh=0, n_top_vars=25, col_celltype=col_celltype,
col_sample=col_sample, fig_title="Age DEGs: Just Space Flight\n\n")
================================================================================ Overall Sample ================================================================================ • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model ================================================================================ Ground Control Subset ================================================================================ • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model ================================================================================ Space Flight Subset ================================================================================ • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model
Condition¶
out_edgr_con_o[0][(out_edgr_con_o[0].adj_p_value < 0.01) & (
out_edgr_con_o[0].abs_log_fc > 0.5)].sort_values(
"abs_log_fc", ascending=False)
Among the older mice, Mir6236 was differentially highly expressed in aged spaceflight mice compared to ground control mice (LFC=1.95, adjusted p=2.61e-13 in the overall sample; LFC=2.11, adjusted p=0.000015 in the aged subset). Suppression of this gene has been linked to neuronal morphogenesis (https://pubs.acs.org/doi/abs/10.1021/acsabm.0c01389) and therefore may represent a candidate intervention target.
Il31ra, which has been linked to neuroinflammation (https://advanced.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202409086), was also more highly expressed in spaceflight mice compared to ground control mice (LFC=1.52, adjusted p<0.001 in the overall sample; LFC=1.53, adjusted p<0.001 in the aged subset).
UBA52, also more highly expressed in spaceflight mice (LFC=1.43, adjusted p<0.001 in the overall sample; LFC=1.45, adjusted p<0.001 in the aged subset), may serve a neuroprotective role: It has been shown underexpressed in PD (https://www.mdpi.com/2073-4409/11/23/3770) and plays a role in neuroautophagy (https://www.tandfonline.com/doi/pdf/10.1080/15548627.2024.2395727). Likewise, Pomp, a gene associated with oxidative stress resistance and protein clearance [CITATION] was also more highly expressed in spaceflight mice. In contrast to past observations of downregulation of Uqcr11 in Alheimer's (https://www.benthamdirect.com/content/journals/car/10.2174/1567205014666170505095921), that gene was differentially highly expressed in spaceflight mice (LFC=1.23 aged subset; LFC=1.44 overall; p<0.001); however, Cmss1, which has been associated with Alzheimer's in APOE4 mice (https://pmc.ncbi.nlm.nih.gov/articles/PMC8064208/), was also more highly expressed in spaceflight mice.
Genes involved in oxidative phosphorylation and the electron transport chain, including Atp5e, Ndufa12, and Uqcr11 were also more highly expressed in spaceflight mice. Uqcr11 has shown an anti-apoptotic and oxidative stress alleviation role in mice (https://www.mdpi.com/2073-4425/16/5/526).
Tpt1 and Rn7sk were also upregulated in spaceflight mice. Tpt1, which is associated with cancer progression, anti-apoptosis, and cell stress response, has been shown to have a potentially bi-directional relationship with cell cycle control gene and canonical senescence marker TP53 (https://www.tandfonline.com/doi/pdf/10.4161/cc.25404). Rn7sk is associated with cellular senescence (https://onlinelibrary.wiley.com/doi/abs/10.1002/jcp.28119) and Alzheimer's (https://www.nature.com/articles/s41598-024-82490-9.pdf).
Pvalb overall sample (LFC=1.61, p=1.25e-11)
Trim17
Cck
Pam16
Mitochondrial/electron transport chain genes whose upregulation has been linked to Parkinson's disease (https://pmc.ncbi.nlm.nih.gov/articles/PMC10259399/pdf/AMS-19-3-131629.pdf) and/or adaptive responses to neurodegeneration in Alheimer's mouse models (https://journals.sagepub.com/doi/abs/10.1177/13872877251314847), including *mt-Atp6, mt-Co1, mt-Cytb mt-Nd2, and mt-Nd5, mt-Nd6...pregulation of *mt-Rnr1, which also showed higher expression in spaceflight aged mice, has been seen in response to ionizing radiation, possibly reflecting increased mitochonrial biogenesis as an adaptive stress response (https://pmc.ncbi.nlm.nih.gov/articles/PMC5555881)**
In [31]:
%matplotlib inline
# Overall
print(f"\n\n{'=' * 80}\nOverall Sample\n{'=' * 80}\n\n")
out_edgr_con = scflow.ax.run_deg_edgr(
self.rna, col_condition, col_covariate=None, formula=None,
**keys[col_condition], log2fc_thresh=0, n_top_vars=25,
col_celltype=col_celltype, col_sample=col_sample,
fig_title=f"{col_condition} DEGs (Overall Sample)\n\n")
out_edgr_con_df = out_edgr_con[0][out_edgr_con[
0].adj_p_value < 0.001].sort_values("abs_log_fc", ascending=False)
goe = out_edgr_con_df.variable[:10].to_list()
print(out_edgr_con_df.iloc[:min(100, out_edgr_con_df.shape[0])], "\n\n", goe)
plt.show()
# Just Old
print(f"\n\n{'=' * 80}\nAged Subset\n{'=' * 80}\n\n")
out_edgr_con_o = scflow.ax.run_deg_edgr(
self.rna[self.rna.obs[col_age] == keys[col_age]["key_treatment"]],
col_condition, col_covariate=None, formula=None,
**keys[col_condition], log2fc_thresh=0, n_top_vars=25,
col_celltype=col_celltype, col_sample=col_sample,
fig_title=f"{col_condition} DEGs (Just Aged)\n\n")
plt.show()
out_edgr_con_o_df = out_edgr_con_o[0][out_edgr_con_o[
0].adj_p_value < 0.001].sort_values("abs_log_fc", ascending=False)
goes = out_edgr_con_o_df.variable[:10].to_list()
print(out_edgr_con_o_df, "\n\n", goes)
_ = self.plot(
kind=["violin", "matrix"], col_celltype=col_batch, layer="log1p",
violin=dict(col_wrap=5, hspace=0.25, rotation=45, figsize=(20, 20)),
matrix=dict(standard_scale="var"), genes=goes)
out_edgr_old_sf_v_gc = out_edgr_con_o_df[0][(
out_edgr_con_o_df[0].abs_log_fc > 1) & (
out_edgr_con_o_df[0].adj_p_value < 0.001)].sort_values(
"adj_p_value", ascending=True)
out_edgr_old_sf_v_gc = out_edgr_old_sf_v_gc.groupby("contrast").apply(
lambda x: x.sort_values("adj_p_value", ascending=True).iloc[
:20]).set_index("variable", append=True).reset_index(
1, drop=True).rename_axis([col_condition, "variable"])
out_edgr_con_compare = out_edgr_con_o_df.set_index("variable")[[
"log_fc", "adj_p_value"]].join(out_edgr_con_df.set_index("variable")[[
"log_fc", "adj_p_value"]], how="inner", lsuffix="_Aged_Subset",
rsuffix="_Overall_Sample")
out_edgr_con_compare
================================================================================ Overall Sample ================================================================================ • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model variable log_fc logCPM F p_value adj_p_value contrast abs_log_fc significance 44 Mir6236 1.949094 8.159038 84.435148 9.711715e-16 2.610725e-13 Space Flight 1.949094 < 0.001 164 Gm48099 1.780488 2.819978 53.266077 1.135266e-11 8.323221e-10 Space Flight 1.780488 < 0.001 130 Gm10076 1.757596 3.404199 59.692926 1.379155e-12 1.273560e-10 Space Flight 1.757596 < 0.001 227 Gm28438 1.678793 5.879479 45.438617 4.617519e-10 2.442226e-08 Space Flight 1.678793 < 0.001 166 Pvalb 1.605108 3.555032 53.957324 1.248318e-11 9.042458e-10 Space Flight 1.605108 < 0.001 6 Atp5e 1.539489 5.428601 117.228802 6.211831e-20 1.073493e-16 Space Flight 1.539489 < 0.001 281 Gm15501 1.537294 1.927398 38.802593 2.979110e-09 1.277954e-07 Space Flight 1.537294 < 0.001 145 Il31ra 1.524226 3.882197 57.257208 4.242210e-12 3.514933e-10 Space Flight 1.524226 < 0.001 278 Gstm5 1.500663 5.850129 41.013165 2.528153e-09 1.096167e-07 Space Flight 1.500663 < 0.001 26 Ndufa1 1.496731 5.073037 95.571980 2.368904e-17 1.058347e-14 Space Flight 1.496731 < 0.001 123 H4c4 1.491993 3.142018 60.776879 8.564019e-13 8.354753e-11 Space Flight 1.491993 < 0.001 7 Uqcr11 1.439361 5.602019 113.035340 2.053137e-19 2.813212e-16 Space Flight 1.439361 < 0.001 29 Uba52 1.428775 4.375851 93.650312 3.198489e-17 1.289738e-14 Space Flight 1.428775 < 0.001 331 Nefh 1.420208 3.309907 34.970474 2.122765e-08 7.734666e-07 Space Flight 1.420208 < 0.001 2 Ndufa4 1.415833 6.724382 126.260441 8.310331e-21 2.841834e-17 Space Flight 1.415833 < 0.001 15 Ndufc1 1.400395 4.990762 106.655939 9.620240e-19 7.273503e-16 Space Flight 1.400395 < 0.001 32 Cox5b 1.397197 6.090833 92.758984 7.015765e-17 2.571809e-14 Space Flight 1.397197 < 0.001 40 Fau 1.383165 6.883136 87.074704 4.178684e-16 1.232916e-13 Space Flight 1.383165 < 0.001 27 Tpt1 1.380907 6.932382 96.630905 2.528130e-17 1.058347e-14 Space Flight 1.380907 < 0.001 161 Pcp4 1.373495 6.702704 55.886023 1.068908e-11 7.981840e-10 Space Flight 1.373495 < 0.001 31 Ndufa13 1.371411 6.366018 92.989430 6.845693e-17 2.571809e-14 Space Flight 1.371411 < 0.001 4 Chchd2 1.362659 6.416984 124.713259 1.174603e-20 2.841834e-17 Space Flight 1.362659 < 0.001 1 Cox7a2 1.353486 6.080846 129.861276 2.954247e-21 1.786876e-17 Space Flight 1.353486 < 0.001 124 Gng13 1.348863 3.651035 61.246960 9.352578e-13 9.051051e-11 Space Flight 1.348863 < 0.001 548 Gstp1 1.344087 3.783787 23.594716 3.062461e-06 6.748013e-05 Space Flight 1.344087 < 0.001 648 Lrrc17 1.331544 2.331744 19.556140 1.720262e-05 3.206473e-04 Space Flight 1.331544 < 0.001 0 Atp5o-1 1.320518 5.595098 131.410251 1.720159e-21 1.786876e-17 Space Flight 1.320518 < 0.001 16 Uqcc2 1.319373 5.034415 106.496327 1.028665e-18 7.319858e-16 Space Flight 1.319373 < 0.001 372 Sdf2l1 1.302962 3.365520 31.683687 8.666740e-08 2.810766e-06 Space Flight 1.302962 < 0.001 22 Ubl5 1.301766 5.235191 101.320761 4.798861e-18 2.523992e-15 Space Flight 1.301766 < 0.001 37 Ndufb7 1.299214 5.226608 89.517411 1.528970e-16 4.790564e-14 Space Flight 1.299214 < 0.001 147 Gpx4 1.298226 5.108516 57.963713 4.411832e-12 3.606076e-10 Space Flight 1.298226 < 0.001 66 Chchd10 1.297640 6.162569 77.841234 6.708275e-15 1.207599e-12 Space Flight 1.297640 < 0.001 33 Ndufb8 1.294489 5.740803 91.829891 8.687932e-17 3.091115e-14 Space Flight 1.294489 < 0.001 52 Ndufs6 1.294290 4.782131 79.735685 2.779925e-15 6.345047e-13 Space Flight 1.294290 < 0.001 13 Uqcrb 1.286523 5.421231 107.852624 8.112226e-19 7.009542e-16 Space Flight 1.286523 < 0.001 288 Gm19951 1.284459 3.149592 39.066613 3.808010e-09 1.593962e-07 Space Flight 1.284459 < 0.001 61 Ndufb11 1.282625 5.510742 78.452429 4.994030e-15 9.602235e-13 Space Flight 1.282625 < 0.001 18 Cox6c 1.282000 6.723627 104.290588 2.851979e-18 1.687364e-15 Space Flight 1.282000 < 0.001 78 Micos13 1.277313 4.594700 72.835243 2.461198e-14 3.768748e-12 Space Flight 1.277313 < 0.001 202 Bbln 1.277230 4.786272 50.254260 6.861508e-11 4.088850e-09 Space Flight 1.277230 < 0.001 34 Tmem160 1.274164 4.172001 89.562878 1.011213e-16 3.495042e-14 Space Flight 1.274164 < 0.001 232 Cck 1.268391 6.751274 45.519797 4.713299e-10 2.447072e-08 Space Flight 1.268391 < 0.001 19 Cox4i1 1.264139 7.466084 104.500190 2.853605e-18 1.687364e-15 Space Flight 1.264139 < 0.001 122 Wdr83os 1.262206 4.001804 61.894153 8.549852e-13 8.354753e-11 Space Flight 1.262206 < 0.001 55 Ndufc2 1.256252 6.139897 79.923690 3.483138e-15 7.524201e-13 Space Flight 1.256252 < 0.001 39 Atp5h 1.254027 6.292460 87.525309 3.453531e-16 1.044434e-13 Space Flight 1.254027 < 0.001 70 Atp5k 1.252296 5.566543 76.009661 1.100038e-14 1.874248e-12 Space Flight 1.252296 < 0.001 102 Tmsb10 1.250417 5.289951 67.662558 1.612022e-13 1.893265e-11 Space Flight 1.250417 < 0.001 38 Pam16 1.250255 4.579989 88.792139 1.544449e-16 4.790564e-14 Space Flight 1.250255 < 0.001 57 Prxl2b 1.247075 3.415394 76.836014 3.824583e-15 7.976893e-13 Space Flight 1.247075 < 0.001 41 Ndufa11 1.244165 5.573681 86.278694 4.457879e-16 1.283975e-13 Space Flight 1.244165 < 0.001 10 Cox6b1 1.242030 6.240567 112.087763 3.150641e-19 3.464845e-16 Space Flight 1.242030 < 0.001 246 Ttc9b 1.237726 4.273295 43.356159 8.735269e-10 4.278160e-08 Space Flight 1.237726 < 0.001 25 Atp5j 1.237422 6.226298 97.530849 1.806729e-17 8.406153e-15 Space Flight 1.237422 < 0.001 30 Cox8a 1.236015 7.200296 94.334628 5.019285e-17 1.958654e-14 Space Flight 1.236015 < 0.001 5 Ndufa8 1.229698 4.908171 117.526732 4.752101e-20 9.581028e-17 Space Flight 1.229698 < 0.001 56 Map1lc3a 1.227905 5.124941 79.209854 3.615474e-15 7.673051e-13 Space Flight 1.227905 < 0.001 9 Atp5g1 1.220064 5.806668 112.290161 2.710636e-19 3.279056e-16 Space Flight 1.220064 < 0.001 62 Cops9 1.216710 4.914995 78.024198 5.000751e-15 9.602235e-13 Space Flight 1.216710 < 0.001 224 Serf1 1.214591 2.496361 44.853315 3.031486e-10 1.629862e-08 Space Flight 1.214591 < 0.001 110 Mien1 1.209610 3.837071 64.964475 2.749663e-13 2.996638e-11 Space Flight 1.209610 < 0.001 36 Hspa8 1.206068 7.295758 90.586066 1.512053e-16 4.790564e-14 Space Flight 1.206068 < 0.001 20 Ndufb5 1.191308 5.680552 103.546784 2.929209e-18 1.687364e-15 Space Flight 1.191308 < 0.001 179 Gm1673 1.178829 4.935595 52.881251 2.676364e-11 1.798665e-09 Space Flight 1.178829 < 0.001 50 Elob 1.174507 5.983606 81.362900 2.184402e-15 5.124952e-13 Space Flight 1.174507 < 0.001 308 Nefm 1.174402 4.795635 37.438837 9.630419e-09 3.770200e-07 Space Flight 1.174402 < 0.001 63 Atp5mpl 1.173997 5.394527 77.937550 5.772956e-15 1.091179e-12 Space Flight 1.173997 < 0.001 24 Tomm7 1.172246 4.844955 97.409397 1.305090e-17 6.315071e-15 Space Flight 1.172246 < 0.001 455 Mt3 1.165726 7.185270 28.152804 4.814190e-07 1.276576e-05 Space Flight 1.165726 < 0.001 54 Ndufa2 1.163015 5.189443 79.570776 3.288112e-15 7.232052e-13 Space Flight 1.163015 < 0.001 525 Fth1 1.160861 8.840357 24.919214 1.933093e-06 4.445747e-05 Space Flight 1.160861 < 0.001 84 Romo1 1.156770 4.675525 72.145065 3.175450e-14 4.519225e-12 Space Flight 1.156770 < 0.001 136 Ubb 1.153242 7.196739 60.310964 2.329261e-12 2.056720e-10 Space Flight 1.153242 < 0.001 160 Bola2 1.149047 3.977584 54.947285 1.037850e-11 7.798058e-10 Space Flight 1.149047 < 0.001 86 Ndufb9 1.147914 5.829289 71.887560 4.363798e-14 6.067685e-12 Space Flight 1.147914 < 0.001 87 Snrpd2 1.145719 4.376915 70.847898 4.482610e-14 6.162061e-12 Space Flight 1.145719 < 0.001 47 Ndufa5 1.145661 5.497964 81.642095 1.840363e-15 4.638097e-13 Space Flight 1.145661 < 0.001 51 Uqcrq 1.145056 5.980211 81.338436 2.203005e-15 5.124952e-13 Space Flight 1.145056 < 0.001 49 Mif 1.143343 5.730586 81.248177 2.176483e-15 5.124952e-13 Space Flight 1.143343 < 0.001 268 Fabp3 1.143316 4.012521 41.212189 1.942679e-09 8.719205e-08 Space Flight 1.143316 < 0.001 263 Krtcap2 1.142985 5.143977 41.899200 1.712171e-09 7.845504e-08 Space Flight 1.142985 < 0.001 11 Cox7c 1.141101 6.771296 110.892853 4.721206e-19 4.759369e-16 Space Flight 1.141101 < 0.001 103 Uqcr10 1.140867 5.384021 67.602655 1.678991e-13 1.952957e-11 Space Flight 1.140867 < 0.001 421 Tmsb4x 1.140484 7.962665 29.616623 2.614201e-07 7.493837e-06 Space Flight 1.140484 < 0.001 194 Ndufs8 1.137055 4.819290 51.065449 5.125605e-11 3.179715e-09 Space Flight 1.137055 < 0.001 76 Ndufb6 1.130561 4.639037 73.241935 2.191407e-14 3.442786e-12 Space Flight 1.130561 < 0.001 74 Eef1b2 1.125756 5.458095 74.579041 1.717882e-14 2.738844e-12 Space Flight 1.125756 < 0.001 71 Cycs 1.124288 4.807677 75.031384 1.278788e-14 2.148541e-12 Space Flight 1.124288 < 0.001 28 Cox5a 1.121024 6.065679 96.279598 2.537163e-17 1.058347e-14 Space Flight 1.121024 < 0.001 23 Slc25a4 1.120911 7.072543 100.334401 8.957301e-18 4.514853e-15 Space Flight 1.120911 < 0.001 138 Tmem14c 1.120603 4.301852 58.941460 2.677887e-12 2.330533e-10 Space Flight 1.120603 < 0.001 336 Gm11478 1.120210 3.535431 34.628592 2.595719e-08 9.317629e-07 Space Flight 1.120210 < 0.001 21 Atp6v1f 1.120100 5.227498 102.375439 3.587833e-18 1.972819e-15 Space Flight 1.120100 < 0.001 557 Gm37194 1.120087 1.909968 22.649994 3.917522e-06 8.492879e-05 Space Flight 1.120087 < 0.001 53 Pfdn5 1.119967 5.493524 79.889945 3.186945e-15 7.139346e-13 Space Flight 1.119967 < 0.001 83 Atp5md 1.119763 6.120894 73.169508 2.986562e-14 4.301005e-12 Space Flight 1.119763 < 0.001 96 Timm8b 1.118199 4.710934 68.676082 1.020067e-13 1.272139e-11 Space Flight 1.118199 < 0.001 158 Ndufa3 1.115852 5.571474 56.292932 8.532050e-12 6.491334e-10 Space Flight 1.115852 < 0.001 94 Hsp90ab1 1.115624 8.086405 70.009669 9.175544e-14 1.164740e-11 Space Flight 1.115624 < 0.001 ['Mir6236', 'Gm48099', 'Gm10076', 'Gm28438', 'Pvalb', 'Atp5e', 'Gm15501', 'Il31ra', 'Gstm5', 'Ndufa1']
================================================================================ Aged Subset ================================================================================ • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model
... storing 'Senescent_Cell_Label_senmayo' as categorical ... storing 'Senescent_Cell_Label_by_Type_senmayo' as categorical ... storing 'Senescent_Cell_Label' as categorical ... storing 'Senescent_Cell_Label_by_Type' as categorical
variable log_fc logCPM F p_value adj_p_value contrast abs_log_fc significance 0 Mir6236 2.063029 8.363280 46.492220 4.055211e-09 0.000049 Space Flight 2.063029 < 0.001 10 Il31ra 1.530428 4.129139 29.232213 7.627201e-07 0.000783 Space Flight 1.530428 < 0.001 1 H4c4 1.511849 3.253100 34.712152 8.279252e-08 0.000274 Space Flight 1.511849 < 0.001 4 Uba52 1.450167 4.645899 32.934334 2.208072e-07 0.000534 Space Flight 1.450167 < 0.001 3 Atp5e 1.242101 5.648002 36.015202 9.050973e-08 0.000274 Space Flight 1.242101 < 0.001 5 Uqcr11 1.237704 5.779053 32.530978 2.989400e-07 0.000603 Space Flight 1.237704 < 0.001 7 Tpt1 1.129632 7.072873 31.454011 4.715708e-07 0.000713 Space Flight 1.129632 < 0.001 9 Cox6c 1.107987 6.893087 30.186732 7.277902e-07 0.000783 Space Flight 1.107987 < 0.001 2 Cmss1 1.052344 8.364766 36.606510 8.653564e-08 0.000274 Space Flight 1.052344 < 0.001 14 Rn7sk 1.045513 7.093615 28.928569 1.142235e-06 0.000921 Space Flight 1.045513 < 0.001 13 Tomm7 1.024909 5.002519 28.859654 1.003420e-06 0.000867 Space Flight 1.024909 < 0.001 6 Lars2 1.013987 7.418589 32.248250 3.630910e-07 0.000627 Space Flight 1.013987 < 0.001 11 Atp5o-1 0.973723 5.687560 29.721979 7.961999e-07 0.000783 Space Flight 0.973723 < 0.001 8 Pomp 0.803001 5.170618 30.690752 5.337545e-07 0.000717 Space Flight 0.803001 < 0.001 12 Ndufa12 0.784367 5.462573 29.512997 8.409691e-07 0.000783 Space Flight 0.784367 < 0.001 ['Mir6236', 'Il31ra', 'H4c4', 'Uba52', 'Atp5e', 'Uqcr11', 'Tpt1', 'Cox6c', 'Cmss1', 'Rn7sk'] None
Out[31]:
| log_fc_Aged_Subset | adj_p_value_Aged_Subset | log_fc_Overall_Sample | adj_p_value_Overall_Sample | |
|---|---|---|---|---|
| variable | ||||
| Mir6236 | 2.063029 | 0.000049 | 1.949094 | 2.610725e-13 |
| Il31ra | 1.530428 | 0.000783 | 1.524226 | 3.514933e-10 |
| H4c4 | 1.511849 | 0.000274 | 1.491993 | 8.354753e-11 |
| Uba52 | 1.450167 | 0.000534 | 1.428775 | 1.289738e-14 |
| Atp5e | 1.242101 | 0.000274 | 1.539489 | 1.073493e-16 |
| Uqcr11 | 1.237704 | 0.000603 | 1.439361 | 2.813212e-16 |
| Tpt1 | 1.129632 | 0.000713 | 1.380907 | 1.058347e-14 |
| Cox6c | 1.107987 | 0.000783 | 1.282000 | 1.687364e-15 |
| Cmss1 | 1.052344 | 0.000274 | 0.992381 | 3.212392e-11 |
| Rn7sk | 1.045513 | 0.000921 | 0.826958 | 1.212908e-06 |
| Tomm7 | 1.024909 | 0.000867 | 1.172246 | 6.315071e-15 |
| Lars2 | 1.013987 | 0.000627 | 0.846444 | 1.512236e-10 |
| Atp5o-1 | 0.973723 | 0.000783 | 1.320518 | 1.786876e-17 |
| Pomp | 0.803001 | 0.000717 | 0.985319 | 4.831120e-16 |
| Ndufa12 | 0.784367 | 0.000783 | 0.981876 | 2.841834e-17 |
Age x Condition¶
Spaceflight effect (main analysis), can visualize at different ages
Chchd2 (related to oxidative phosphorylation, cell migration, apoptosis inhibition, COX regulation, stress adaptation, Parkinson's, Lewy Body diseases, and cancer) was more highly expressed in spaceflight mice (and demonstrated a bigger young versus old difference in spaceflight mice). Spaceflight mice also showed higher expression of several ribosomal proteins (Rps16, Rpl31, Rpl17, Rpl9, Rpl15, Rps15a, Rpl19, Rps7, Rps23, Rpl37, Rps3a1, Rpl29, Rps15, Rps14).
Ndufa4 was also more highly expressed (implicated in cancer and mitochondrial dysfunction, part of the cytochrome c oxidase complex which drives oxidative phosphorylation, adaptation to hypoxia via downregulation of oxygen consumption and suppression of mitochondrial ROS production https://www.cell.com/cell-metabolism/fulltext/S1550-4131(11)00394-9?mobileUi=0) in spaceflight mice, as were other genes involved in ATP production/the electron transport chain and oxidative phosophorylation, including Cox7a2, Uqcr11, Ndufa12, Ndufa4, Ndufc1, Atp5o-1, Atp5e, and Uqcrh (which additionally has been implicated in tumor suppression https://aacrjournals.org/cancerres/article/80/16_Supplement/4795/643367).
In [61]:
# With Single Label for Age x Condition
print(f"\n\n{'=' * 80}\nGroup (Single Factor)\n{'=' * 80}\n\n")
out_edgr_groups = scflow.ax.run_deg_edgr(
self.rna, col_batch, col_covariate=None, formula=None,
**keys[col_batch], col_sample=col_sample,
log2fc_thresh=0, n_top_vars=25, col_celltype=col_celltype)
print(list(out_edgr_groups[0].variable[:25]))
plt.show()
# _ = self.plot(kind="violin", col_celltype=col_batch,
# genes=list(out_edgr_groups[0].variable[:25]), layer="scaled",
# violin=dict(col_wrap=4, hspace=1, rotation=45))
out_edgr_batches = out_edgr_groups[0][(out_edgr_groups[0].abs_log_fc > 1) & (
out_edgr_groups[0].adj_p_value < 0.001)].sort_values(
"adj_p_value", ascending=True)
out_edgr_batches_top = out_edgr_batches.groupby("contrast").apply(
lambda x: x.sort_values("adj_p_value", ascending=True).iloc[
:20]).set_index("variable", append=True).reset_index(
1, drop=True).rename_axis([col_condition, "variable"])
# With Contrasts (Age & Condition = Separate Factors)
print(f"\n\n{'=' * 80}\nAge * Condition (2-Factor Contrast)\n{'=' * 80}\n\n")
out_edgr_contrasts = scflow.ax.run_deg_edgr(
self.rna, col_condition, col_covariate=col_age, formula=None,
**keys[col_condition], log2fc_thresh=0, n_top_vars=24,
col_celltype=col_celltype, col_sample=col_sample)
_ = self.plot(
kind=["violin", "matrix"], col_celltype=col_batch,
genes=out_edgr_contrasts[0][out_edgr_contrasts[
0].adj_p_value < 0.001].sort_values(
"abs_log_fc", ascending=False).variable[:10], layer="log1p",
violin=dict(col_wrap=5, hspace=0.25, rotation=45, figsize=(20, 20)),
matrix=dict(standard_scale="var"))
================================================================================ Group (Single Factor) ================================================================================ • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model ['Ndufs8', 'Taco1', 'Uqcrb', 'Atp5e', 'Spcs1', 'Ubqln2', 'Snrpd2', 'Stk35', 'Psenen', 'Ndufa4', 'Mif', 'Tmsb10', 'AY036118', 'Atp6v0c', 'Chchd10', 'Cox7c', 'Ndufa13', 'Hsp90ab1', 'Ndufb11', 'Ddost', 'Ubb', 'Sco2', 'Bet1l', 'Atp5o-1', 'Ndufb10']
================================================================================ Age * Condition (2-Factor Contrast) ================================================================================ • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Performing pseudobulk for paired samples
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
None
Condition x Age¶
Vulnerability of mice who were older at launch to spaceflight stress
In [36]:
# With Contrasts (Age & Condition = Separate Factors)
out_edgr_contrasts_age = scflow.ax.run_deg_edgr(
self.rna, col_age, col_covariate=col_condition, formula=None,
**keys[col_age], log2fc_thresh=0, n_top_vars=24,
col_celltype=col_celltype, col_sample=col_sample)
• Calculating NormFactors • Estimating Dispersions • Fitting linear model • Performing pseudobulk for paired samples
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
Senescence Gene Contrasts Results¶
In [37]:
# Genes
sen_genes_in_contrasts = list(set(genes).intersection(
out_edgr_contrasts[0].variable.unique())) # senescence genes in contrasts
out_edgr_snc = out_edgr_contrasts[0].set_index(
"variable").loc[sen_genes_in_contrasts] # contrast results for snc genes
res_snc_contrast = out_edgr_snc[out_edgr_snc.adj_p_value < 0.01] # p < 0.01
sig_contrast_genes = list(res_snc_contrast.index.values) # significant genes
# Plots By Groups; Axis by Cell Types
kws = dict(dot=dict(figsize=(10, 5)), heat=dict(figsize=(15, 15)))
_ = self.plot(kind=["heat", "dot"], genes=sig_contrast_genes,
standard_scale="obs", col_celltype=col_celltype,
by_group=col_batch, **kws)
# Plots By Cell Types; Axis by Groups
# kws = dict(dot=dict(figsize=(10, 5)), heat=dict(figsize=(15, 15)),
# violin=dict(figsize=(30, 20), col_wrap=4, xlabel=None,
# rotation=45, hspace=1, top=0.95))
kws = dict(dot=dict(figsize=(10, 5)), heat=dict(figsize=(15, 15)))
_ = self.plot(kind=["heat", "dot"], genes=sig_contrast_genes,
standard_scale="obs", col_celltype=col_batch,
by_group=col_celltype, **kws)
# Results
print(f"\n{'=' * 80}\nSignificant Senescence Gene Contrasts\n{'=' * 80}\n")
print(sig_contrast_genes)
res_snc_contrast
WARNING: Unknown type for standard_scale, ignored
WARNING: Unknown type for standard_scale, ignored
WARNING: Unknown type for standard_scale, ignored
WARNING: Unknown type for standard_scale, ignored
WARNING: Unknown type for standard_scale, ignored
WARNING: Unknown type for standard_scale, ignored
WARNING: Unknown type for standard_scale, ignored
WARNING: Unknown type for standard_scale, ignored
WARNING: Unknown type for standard_scale, ignored
WARNING: Unknown type for standard_scale, ignored
WARNING: Unknown type for standard_scale, ignored
================================================================================ Significant Senescence Gene Contrasts ================================================================================ ['Pigf', 'Bex3', 'Hmgb1', 'Mif']
Out[37]:
| log_fc | logCPM | F | p_value | adj_p_value | contrast | abs_log_fc | |
|---|---|---|---|---|---|---|---|
| variable | |||||||
| Pigf | 0.351961 | 3.954538 | 13.143226 | 4.024638e-04 | 5.212639e-03 | None | 0.351961 |
| Bex3 | 0.953014 | 5.195395 | 42.328285 | 1.485855e-09 | 7.042051e-08 | None | 0.953014 |
| Hmgb1 | 0.400718 | 6.081534 | 16.838421 | 7.199312e-05 | 1.145922e-03 | None | 0.400718 |
| Mif | 1.143501 | 5.730556 | 82.364941 | 1.637643e-15 | 4.402347e-13 | None | 1.143501 |
SnC DEGs¶
SnCs vs. Not¶
Slc23a2
Overall¶
In [32]:
out_edgr_contrasts_snc = scflow.ax.run_deg_edgr(
self.rna, "Senescent_Cell_Label",
col_covariate=col_batch, formula=None,
key_treatment="Senescent", key_control="Non-Senescent",
log2fc_thresh=0, n_top_vars=24, xlabel_rotation=45,
hspace=0.5, wspace=0.2, top=0.92, legend_loc="upper right",
col_celltype=col_celltype, col_sample=col_sample)
• Calculating NormFactors • Estimating Dispersions • Fitting linear model • Performing pseudobulk for paired samples
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
Rank Genes Groups¶
In [225]:
res_rank_genes, p_threshold = {}, 1e-10
for x in self.rna.obs[col_batch].unique():
res_rank_genes[x] = {}
for c in self.rna.obs[self.rna.obs[col_batch] == x][col_celltype].unique():
s_iter = (self.rna.obs[col_batch] == x) & (self.rna.obs[
"Senescent_Cell_Label_by_Type"].isin([c, f"SnC {c}"]))
tmp = sc.tl.rank_genes_groups(
self.rna[s_iter], "Senescent_Cell_Label_by_Type", rankby_abs=True,
layer="log1p", groups=[c, f"SnC {c}"], reference=c, copy=True)
res_rank_genes[x][c] = sc.get.rank_genes_groups_df(
tmp, f"SnC {c}", pval_cutoff=p_threshold)
tmp = sc.tl.rank_genes_groups(
self.rna[self.rna.obs[col_batch] == x],
"Senescent_Cell_Label", rankby_abs=True,
layer="log1p", reference="Non-Senescent", copy=True)
res_rank_genes[x]["Overall"] = sc.get.rank_genes_groups_df(
tmp, f"Senescent", pval_cutoff=p_threshold)
res_rank_genes[x] = pd.concat(res_rank_genes[x], names=[col_celltype])
res_rank_genes = pd.concat(res_rank_genes, names=[col_batch])
res_rank_genes = res_rank_genes.assign(
logfoldchanges_abs=res_rank_genes.logfoldchanges.abs())
res_rank_genes_top = res_rank_genes.reset_index().groupby([
col_batch, col_celltype]).apply(
lambda x: x.sort_values("logfoldchanges_abs").iloc[:20],
include_groups=False).reset_index(2, drop=True).set_index(
"names", append=True).rename_axis([
col_batch, col_celltype, "variable"])
res_rank_genes_top
... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical ... storing 'scCODA_sample_id' as categorical
Out[225]:
| level_2 | scores | logfoldchanges | pvals | pvals_adj | logfoldchanges_abs | |||
|---|---|---|---|---|---|---|---|---|
| Group | annotation_by_overlap | variable | ||||||
| Ground Control | 12 Weeks | Astrocyte | Paqr8 | 582 | -11.625196 | -3.957662 | 2.459272e-13 | 1.731654e-12 | 3.957662 |
| Brinp3 | 765 | -10.685084 | -3.974858 | 1.843256e-12 | 1.225158e-11 | 3.974858 | ||
| Nrxn2 | 744 | -10.750609 | -4.002379 | 2.766084e-13 | 1.937540e-12 | 4.002379 | ||
| Fgf14 | 563 | -11.757295 | -4.031304 | 2.039633e-13 | 1.445581e-12 | 4.031304 | ||
| Grm3 | 834 | -10.358327 | -4.042312 | 6.421581e-12 | 4.112328e-11 | 4.042312 | ||
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| Space Flight | 29 Weeks | Overall | Ccser1 | 184 | -7.551204 | -0.874172 | 2.517150e-13 | 1.645944e-11 | 0.874172 |
| Prickle1 | 215 | -7.282249 | -0.879054 | 1.517610e-12 | 8.499319e-11 | 0.879054 | ||
| Kcnq1ot1 | 176 | -7.600009 | -0.881908 | 1.812542e-13 | 1.224934e-11 | 0.881908 | ||
| Kalrn | 197 | -7.432727 | -0.883547 | 5.562165e-13 | 3.398258e-11 | 0.883547 | ||
| Dst | 113 | -8.208591 | -0.890387 | 2.550492e-15 | 2.659767e-13 | 0.890387 |
640 rows × 6 columns
By Condition (Just Aged)¶
In [143]:
# Ager Subset
subset_old = self.rna.obs[col_age] == keys[col_age]["key_treatment"]
# Old Ground Control SnCs
print(f"\n\n{'=' * 80}\nGround Control (Aged)\n{'=' * 80}\n\n")
subset_gc = self.rna.obs[col_condition] == keys[
col_condition]["key_control"]
out_edgr_contrasts_snc_gc = scflow.ax.run_deg_edgr(
self.rna[subset_gc & subset_old], "Senescent_Cell_Label",
key_treatment="Senescent", top=0.92,
key_control="Non-Senescent", log2fc_thresh=0, n_top_vars=24,
xlabel_rotation=45, hspace=0.5, wspace=0.2, legend_loc="upper right",
col_celltype=col_celltype, col_sample=col_sample)
if len(self.rna[subset_gc & subset_old].obs[col_batch].unique()) > 1:
raise ValueError("Check subsetting")
key_1 = self.rna[subset_gc & subset_old].obs[col_batch].unique()[0]
# Old Spaceflight SnCs
print(f"\n\n{'=' * 80}\nSpaceflight (Aged)\n{'=' * 80}\n\n")
subset_sf = self.rna.obs[col_condition] == keys[
col_condition]["key_treatment"]
out_edgr_contrasts_snc_sf = scflow.ax.run_deg_edgr(
self.rna[subset_sf & subset_old], "Senescent_Cell_Label",
key_treatment="Senescent", key_control="Non-Senescent",
log2fc_thresh=0, n_top_vars=24, xlabel_rotation=45,
hspace=0.5, wspace=0.2, top=0.92, legend_loc="upper right",
col_celltype=col_celltype, col_sample=col_sample)
if len(self.rna[subset_sf & subset_old].obs[col_batch].unique()) > 1:
raise ValueError("Check subsetting")
key_2 = self.rna[subset_sf & subset_old].obs[col_batch].unique()[0]
# Compare
res_snc_gc = out_edgr_contrasts_snc_gc[0][out_edgr_contrasts_snc_gc[
0].adj_p_value < 0.001].sort_values("adj_p_value").set_index("variable")
res_snc_sf = out_edgr_contrasts_snc_sf[0][out_edgr_contrasts_snc_sf[
0].adj_p_value < 0.001].sort_values("adj_p_value").set_index("variable")
res_snc_grps = pd.concat([
res_snc_gc, res_snc_sf], keys=[key_1, key_2], names=[col_condition])
res_snc_grps = res_snc_grps[res_snc_grps.abs_log_fc >= 1]
# top_tx_g_snc = res_snc_grps.loc[keys[col_condition][
# "key_treatment"]].sort_values("abs_log_fc", ascending=False).index
# print(res_snc_grps.unstack(0).replace(np.nan, "")[[
# "log_fc", "adj_p_value"]].loc[top_tx_g_snc])
# just_sf = res_snc_grps.loc[keys[col_condition][
# "key_treatment"]].index.difference(res_snc_grps.loc[keys[
# col_condition]["key_control"]].index)
# just_gc = res_snc_grps.loc[keys[col_condition][
# "key_control"]].index.difference(res_snc_grps.loc[keys[
# col_condition]["key_treatment"]].index)
# print(res_snc_grps.loc[:, just_gc, :].sort_values(
# "abs_log_fc", ascending=False)[["log_fc", "adj_p_value"]], "\n\n")
# res_snc_grps.loc[:, just_sf, :].sort_values(
# "abs_log_fc", ascending=False)[["log_fc", "adj_p_value"]]
res_snc_grps.unstack(0).replace(np.nan, "")[["log_fc", "adj_p_value"]]
================================================================================ Spaceflight (Aged) ================================================================================ • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) File ~/miniconda3/envs/rsc/lib/python3.13/site-packages/pandas/core/indexes/base.py:3805, in Index.get_loc(self, key) 3804 try: -> 3805 return self._engine.get_loc(casted_key) 3806 except KeyError as err: File index.pyx:167, in pandas._libs.index.IndexEngine.get_loc() File index.pyx:196, in pandas._libs.index.IndexEngine.get_loc() File pandas/_libs/hashtable_class_helper.pxi:7081, in pandas._libs.hashtable.PyObjectHashTable.get_item() File pandas/_libs/hashtable_class_helper.pxi:7089, in pandas._libs.hashtable.PyObjectHashTable.get_item() KeyError: 'Space Flight' The above exception was the direct cause of the following exception: KeyError Traceback (most recent call last) Cell In[143], line 40 37 res_snc_grps = pd.concat([ 38 res_snc_gc, res_snc_sf], keys=[key_1, key_2], names=[col_condition]) 39 res_snc_grps = res_snc_grps[res_snc_grps.abs_log_fc >= 1] ---> 40 top_tx_g_snc = res_snc_grps.loc[keys[col_condition][ 41 "key_treatment"]].sort_values("abs_log_fc", ascending=False).index 42 # print(res_snc_grps.unstack(0).replace(np.nan, "")[[ 43 # "log_fc", "adj_p_value"]].loc[top_tx_g_snc]) 44 # just_sf = res_snc_grps.loc[keys[col_condition][ (...) 52 # res_snc_grps.loc[:, just_sf, :].sort_values( 53 # "abs_log_fc", ascending=False)[["log_fc", "adj_p_value"]] 54 res_snc_grps.unstack(0).replace(np.nan, "")[["log_fc", "adj_p_value"]] File ~/miniconda3/envs/rsc/lib/python3.13/site-packages/pandas/core/indexing.py:1191, in _LocationIndexer.__getitem__(self, key) 1189 maybe_callable = com.apply_if_callable(key, self.obj) 1190 maybe_callable = self._check_deprecated_callable_usage(key, maybe_callable) -> 1191 return self._getitem_axis(maybe_callable, axis=axis) File ~/miniconda3/envs/rsc/lib/python3.13/site-packages/pandas/core/indexing.py:1431, in _LocIndexer._getitem_axis(self, key, axis) 1429 # fall thru to straight lookup 1430 self._validate_key(key, axis) -> 1431 return self._get_label(key, axis=axis) File ~/miniconda3/envs/rsc/lib/python3.13/site-packages/pandas/core/indexing.py:1381, in _LocIndexer._get_label(self, label, axis) 1379 def _get_label(self, label, axis: AxisInt): 1380 # GH#5567 this will fail if the label is not present in the axis. -> 1381 return self.obj.xs(label, axis=axis) File ~/miniconda3/envs/rsc/lib/python3.13/site-packages/pandas/core/generic.py:4293, in NDFrame.xs(self, key, axis, level, drop_level) 4290 index = self.index 4292 if isinstance(index, MultiIndex): -> 4293 loc, new_index = index._get_loc_level(key, level=0) 4294 if not drop_level: 4295 if lib.is_integer(loc): 4296 # Slice index must be an integer or None File ~/miniconda3/envs/rsc/lib/python3.13/site-packages/pandas/core/indexes/multi.py:3290, in MultiIndex._get_loc_level(self, key, level) 3288 return indexer, maybe_mi_droplevels(indexer, ilevels) 3289 else: -> 3290 indexer = self._get_level_indexer(key, level=level) 3291 if ( 3292 isinstance(key, str) 3293 and self.levels[level]._supports_partial_string_indexing 3294 ): 3295 # check to see if we did an exact lookup vs sliced 3296 check = self.levels[level].get_loc(key) File ~/miniconda3/envs/rsc/lib/python3.13/site-packages/pandas/core/indexes/multi.py:3391, in MultiIndex._get_level_indexer(self, key, level, indexer) 3388 return slice(i, j, step) 3390 else: -> 3391 idx = self._get_loc_single_level_index(level_index, key) 3393 if level > 0 or self._lexsort_depth == 0: 3394 # Desired level is not sorted 3395 if isinstance(idx, slice): 3396 # test_get_loc_partial_timestamp_multiindex File ~/miniconda3/envs/rsc/lib/python3.13/site-packages/pandas/core/indexes/multi.py:2980, in MultiIndex._get_loc_single_level_index(self, level_index, key) 2978 return -1 2979 else: -> 2980 return level_index.get_loc(key) File ~/miniconda3/envs/rsc/lib/python3.13/site-packages/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key) 3807 if isinstance(casted_key, slice) or ( 3808 isinstance(casted_key, abc.Iterable) 3809 and any(isinstance(x, slice) for x in casted_key) 3810 ): 3811 raise InvalidIndexError(key) -> 3812 raise KeyError(key) from err 3813 except TypeError: 3814 # If we have a listlike key, _check_indexing_error will raise 3815 # InvalidIndexError. Otherwise we fall through and re-raise 3816 # the TypeError. 3817 self._check_indexing_error(key) KeyError: 'Space Flight'
SnC as Covariate¶
In [33]:
out_edgr_contrasts_snc_cov = {}
for a in self.rna.obs[col_age].unique():
print(f"\n\n{'=' * 80}\nAge = {a}\n{'=' * 80}\n\n")
out_edgr_contrasts_snc_cov[a] = scflow.ax.run_deg_edgr(
self.rna[self.rna.obs[col_age] == a], col_condition,
col_covariate="Senescent_Cell_Label",
**keys[col_condition], log2fc_thresh=0, n_top_vars=24,
xlabel_rotation=45, hspace=0.5, wspace=0.2, top=0.85,
legend_loc="upper right", col_celltype=col_celltype,
col_sample=col_sample)
plt.show()
================================================================================ Age = 20 Weeks ================================================================================ • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Performing pseudobulk for paired samples
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
================================================================================ Age = 37 Weeks ================================================================================ • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Performing pseudobulk for paired samples
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/seaborn/categorical.py:700: PendingDeprecationWarning: vert: bool will be deprecated in a future version. Use orientation: {'vertical', 'horizontal'} instead.
artists = ax.bxp(**boxplot_kws)
Just SnCs¶
All Groups SnCs¶
In [ ]:
out_edgr_groups_snc = scflow.ax.run_deg_edgr(
self.rna[(self.rna.obs["Senescent_Cell_Label"] == "Senescent")],
col_batch, **keys[col_batch], col_sample=col_sample,
log2fc_thresh=0, n_top_vars=25, col_celltype=col_celltype)
print(list(out_edgr_groups[0].variable[:25]))
out_edgr_batches_snc = out_edgr_groups[0][(
out_edgr_groups[0].abs_log_fc > 1) & (
out_edgr_groups[0].adj_p_value < 0.001)].sort_values(
"adj_p_value", ascending=True)
out_edgr_batches_top = out_edgr_batches.groupby("contrast").apply(
lambda x: x.sort_values("adj_p_value", ascending=True).iloc[
:20]).set_index("variable", append=True).reset_index(
1, drop=True).rename_axis([col_condition, "variable"])
plt.show()
• Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model ['Ndufs8', 'Taco1', 'Uqcrb', 'Atp5e', 'Spcs1', 'Ubqln2', 'Snrpd2', 'Stk35', 'Psenen', 'Ndufa4', 'Mif', 'Tmsb10', 'AY036118', 'Atp6v0c', 'Chchd10', 'Cox7c', 'Ndufa13', 'Hsp90ab1', 'Ndufb11', 'Ddost', 'Ubb', 'Sco2', 'Bet1l', 'Atp5o-1', 'Ndufb10']
/tmp/ipykernel_939743/4169309774.py:10: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
out_edgr_batches_top = out_edgr_batches.groupby("contrast").apply(
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[236], line 14 6 out_edgr_batches_snc = out_edgr_groups[0][( 7 out_edgr_groups[0].abs_log_fc > 1) & ( 8 out_edgr_groups[0].adj_p_value < 0.001)].sort_values( 9 "adj_p_value", ascending=True) 10 out_edgr_batches_top = out_edgr_batches.groupby("contrast").apply( 11 lambda x: x.sort_values("adj_p_value", ascending=True).iloc[ 12 :20]).set_index("variable", append=True).reset_index( 13 1, drop=True).rename_axis([col_condition, "variable"]) ---> 14 plt.show(dpi=200) File ~/miniconda3/envs/rsc/lib/python3.13/site-packages/matplotlib/pyplot.py:614, in show(*args, **kwargs) 570 """ 571 Display all open figures. 572 (...) 611 explicitly there. 612 """ 613 _warn_if_gui_out_of_main_thread() --> 614 return _get_backend_mod().show(*args, **kwargs) TypeError: show() got an unexpected keyword argument 'dpi'
SF vs. Aged GC¶
In [98]:
# subs_old = self.rna.obs[col_age] == keys[col_age]["key_treatment"]
# subs_sf = self.rna.obs[col_condition] == keys[
# col_condition]["key_treatment"]
# subs_noynggc_snc = (self.rna.obs[
# "Senescent_Cell_Label"] == "Senescent") & (
# subs_old | subs_sf)
# # ktx = keys[col_batch]["key_treatment"][1:]
# ktx = [keys[col_batch]["key_control"]] + keys[
# col_batch]["key_treatment"][1:]
# out_edgr_groups_snc = scflow.ax.run_deg_edgr(
# # self.rna[subs_noynggc_snc],
# self.rna,
# col_batch, col_sample=col_sample,
# key_control=keys[col_batch]["key_treatment"][0],
# key_treatment=ktx,
# log2fc_thresh=0, n_top_vars=25, col_celltype=col_celltype)
# print(list(out_edgr_groups[0].variable[:25]))
# out_edgr_batches_snc = out_edgr_groups[0][(
# out_edgr_groups[0].abs_log_fc > 1) & (
# out_edgr_groups[0].adj_p_value < 0.001)].sort_values(
# "adj_p_value", ascending=True)
# out_edgr_batches_top = out_edgr_batches.groupby("contrast").apply(
# lambda x: x.sort_values("adj_p_value", ascending=True).iloc[
# :20], include_groups=False).set_index(
# "variable", append=True).reset_index(
# 1, drop=True).rename_axis([col_condition, "variable"])
# plt.show()
• Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model ['Ndufs8', 'Taco1', 'Uqcrb', 'Atp5e', 'Spcs1', 'Ubqln2', 'Snrpd2', 'Stk35', 'Psenen', 'Ndufa4', 'Mif', 'Tmsb10', 'AY036118', 'Atp6v0c', 'Chchd10', 'Cox7c', 'Ndufa13', 'Hsp90ab1', 'Ndufb11', 'Ddost', 'Ubb', 'Sco2', 'Bet1l', 'Atp5o-1', 'Ndufb10']
Just Aged SnCs¶
In [28]:
out_edgr_contrasts_sncto, snc_degs_by_condition_age_ct = {}, {}
thresh_p_c, thresh_lfcabs_c = 0.05, 0.5 # thresholds
subs_old = self.rna.obs[col_age] == keys[col_age]["key_treatment"]
subs_sf = self.rna.obs[col_condition] == keys[col_condition]["key_treatment"]
# subs_ac = (self.rna.obs["Senescent_Cell_Label"] == "Senescent") & (
# subs_old | subs_sf)
subs_ac = (self.rna.obs["Senescent_Cell_Label"] == "Senescent") & (subs_old)
out_edgr_contrasts_sncto["Overall"] = scflow.ax.run_deg_edgr(
self.rna[subs_ac], col_condition,
# col_covariate=col_age,
**keys[col_condition],
log2fc_thresh=0, n_top_vars=24,
legend_loc="upper center", fig_title=f"Overall",
wspace=0.25, bottom=0.3, hspace=1 if len(x) > 10 else 0.4, top=0.95,
kws_xticks=dict(fontsize=10, rotation=45 if len(x) > 10 else None),
col_celltype=col_celltype, col_sample=col_sample)
plt.show()
tmp = out_edgr_contrasts_sncto["Overall"][0]
tmp = tmp[(tmp.adj_p_value < thresh_p_c) & (
tmp.abs_log_fc >= thresh_lfcabs_c)] # filter by p & lfc
snc_degs_by_condition_age_ct["Overall"] = list(
tmp.sort_values("adj_p_value", ascending=True).variable)
for x in self.rna.obs[col_celltype].unique():
print(x)
out_edgr_contrasts_sncto[x] = scflow.ax.run_deg_edgr(
self.rna[(subs_ac) & (self.rna.obs[col_celltype] == x)],
col_condition,
# col_covariate=col_age,
**keys[col_condition],
log2fc_thresh=0, n_top_vars=24,
legend_loc="upper center", fig_title=f"Senescent {x}",
wspace=0.25, bottom=0.3, hspace=1 if len(x) > 10 else 0.4, top=0.95,
kws_xticks=dict(fontsize=10, rotation=45 if len(x) > 10 else None),
col_celltype=col_celltype, col_sample=col_sample)
plt.show()
tmp = out_edgr_contrasts_sncto[x][0]
tmp = tmp[(tmp.adj_p_value < thresh_p_c) & (
tmp.abs_log_fc >= thresh_lfcabs_c)] # filter by p & lfc
snc_degs_by_condition_age_ct[x] = list(tmp.sort_values(
"adj_p_value", ascending=True).variable)
del tmp
out_edgr_contrasts_snc_all_top = pd.concat([out_edgr_contrasts_sncto[x][
0].set_index("variable").loc[snc_degs_by_condition_age_ct[
x]] for x in out_edgr_contrasts_sncto], keys=out_edgr_contrasts_sncto,
names=[col_celltype])
out_edgr_contrasts_snc_all = pd.concat([
out_edgr_contrasts_sncto[x][0].set_index("variable")
for x in out_edgr_contrasts_sncto], keys=out_edgr_contrasts_sncto,
names=[col_celltype])
out_edgr_contrasts_snc_all_top
• Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model
Excitatory • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model
Excitatory-Inhibitory • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model
Oligodendrocyte • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model
Inhibitory • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model
Microglial • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model
OPC • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model
Astrocyte • Calculating NormFactors • Estimating Dispersions • Fitting linear model • Calculating NormFactors • Estimating Dispersions • Fitting linear model
Out[28]:
| log_fc | logCPM | F | p_value | adj_p_value | contrast | abs_log_fc | significance | ||
|---|---|---|---|---|---|---|---|---|---|
| annotation_by_overlap | variable | ||||||||
| Excitatory | Mir6236 | 2.785393 | 8.852131 | 24.576078 | 7.684583e-07 | 0.009296 | Space Flight | 2.785393 | < 0.01 |
| Astrocyte | Slc4a10 | -4.664966 | 9.805859 | 32.261728 | 1.393764e-08 | 0.000169 | Space Flight | 4.664966 | < 0.001 |
| Ccn3 | -4.767686 | 9.181761 | 28.629274 | 9.003095e-08 | 0.000409 | Space Flight | 4.767686 | < 0.001 | |
| Chrm3 | -6.454060 | 8.549329 | 28.399240 | 1.013490e-07 | 0.000409 | Space Flight | 6.454060 | < 0.001 | |
| Rorb | 6.667245 | 9.297352 | 22.394432 | 2.257624e-06 | 0.006828 | Space Flight | 6.667245 | < 0.01 | |
| Rspo3 | -3.907725 | 8.962092 | 21.132434 | 4.350717e-06 | 0.010526 | Space Flight | 3.907725 | < 0.1 | |
| Fxyd5 | -4.017004 | 8.674347 | 19.493648 | 1.022373e-05 | 0.020613 | Space Flight | 4.017004 | < 0.1 | |
| Bche | -5.609772 | 8.050771 | 18.246627 | 1.962775e-05 | 0.033920 | Space Flight | 5.609772 | < 0.1 | |
| Tmeff2 | -3.229761 | 9.214059 | 17.357692 | 3.128428e-05 | 0.047306 | Space Flight | 3.229761 | < 0.1 |
Pathway Analysis: SnC DEGs¶
In [102]:
# Pathway Enrichment Analysis
names = gp.get_library_name()
# [i for i in names if "brain" in i.lower() or "mouse" in i.lower()]
pathways_snc, res_pathways_snc = {}, {}
thresh_p_path = 0.05
# perts_geo = ["Aging", "Drug", "Disease"]
perts_geo = ["Aging", "Disease"]
other_sets = ["GTEx_Aging_Signatures_2021", f"HDSigDB_{species}_2021",
# "DGIdb_Drug_Targets_2024",
"MSigDB_Hallmark_2020",
"MSigDB_Oncogenic_Signatures", "OMIM_Disease",
# "Reactome_Pathways_2024", "Reactome_2022",
f"WikiPathways_2024_{species}", f"WikiPathways_2019_{species}"]
other_sets += [i for i in names if "GO_Biological_Process" in i]
# if species.lower() == "mouse":
# other_sets += ["PerturbAtlas_MouseGenePerturbationSigs"]
for x in out_edgr_contrasts_snc_all_top.reset_index()[col_celltype].unique():
pathways_snc[x], res_pathways_snc[x] = {}, {}
for i in ["up", "down"]:
degs_tmp = out_edgr_contrasts_snc_all_top.loc[x]
if i == "up":
degs_tmp = list(degs_tmp[degs_tmp.log_fc > 0].index.values)
else:
degs_tmp = list(degs_tmp[degs_tmp.log_fc < 0].index.values)
if len(degs_tmp) > 0:
g_s = [f"{u}_Perturbations_from_GEO_{i}" for u in perts_geo] + [
f"Disease_Signatures_from_GEO_{i}_2014"] + other_sets
g_s = [g for g in g_s if g in names]
pathways_snc[x][i] = gp.enrichr(
gene_list=degs_tmp, gene_sets=g_s,
organism=species, cutoff=0.5)
# fig = gp.barplot(pathways_snc[x][i].res2d, title=f"{x} ({i})",
# color="r" if i == "up" else "b")
res_pathways_snc[x][i] = pathways_snc[x][i].results[pathways_snc[
x][i].results["Adjusted P-value"] < thresh_p_path]
res_pathways_snc[x] = pd.concat(res_pathways_snc[x], names=["Direction"])
res_pathways_snc = pd.concat(res_pathways_snc, names=[col_celltype])
res_pathways_snc.loc[:, "Term Short"] = res_pathways_snc.Term.apply(
lambda x: re.sub("Expression Of ", "", re.sub(".*.xls", "", re.sub(
".*.XLSX", "", re.sub(".*Supplementary Table [0-9]+[-]?", "", re.sub(
".*.xlsx.", "", re.sub(".*Supplementary Data ..", "", re.sub(
"PMID([0-9])+", "", re.sub(
"([0-9]+) years v ([0-9]+) years", "\\1 v \\2 yrs.",
re.sub("aging:([0-9]+)", "aging", re.sub(
"([0-9]+) months v ([0-9]+) mo(nths)?",
"\\1 v \\2 mo.", re.sub("SRP[0-9]+ ", "", re.sub(
"GSE[0-9]+ ", "", x)))))))))))))
cts_i = res_pathways_snc.reset_index()[col_celltype].unique()
res_pathways_snc_top = res_pathways_snc.reset_index().groupby([
col_celltype, "Direction"]).apply(lambda x: x.sort_values(
"Adjusted P-value", ascending=True).head(10), include_groups=False)
# res_pathways_snc_top = res_pathways_snc_top[res_pathways_snc_top[
# "Gene_set"] != "PheWeb_2019"]
# Plot/Display
fig, axes = plt.subplots(*scflow.pl.square_grid(cts_i), figsize=(
20, 25), gridspec_kw=dict(wspace=1.5, top=0.95), squeeze=False)
for i, x in enumerate(cts_i):
gp.dotplot(
res_pathways_snc_top.loc[x].reset_index(),
column="Adjusted P-value",
# column="Overlap",
x="Direction", y="Term Short",
ax=axes.flatten()[i], size=20, top_term=100,
title=x, xticklabels_rot=45, yticklabels_rot=45,
show_ring=True, marker="o", cutoff=0.5)
axes.flatten()[i].tick_params(axis="y", labelsize=8,
labelfontfamily="serif")
axes.flatten()[i].tick_params(axis="x", labelsize=10,
labelfontfamily="serif")
axes.flatten()[i].title.set_fontsize(10)
if len(axes.flatten()) > len(cts_i):
for a in axes.flatten()[len(cts_i):]:
a.set_visible(False)
fig.suptitle("Senescent Cell Spaceflight DEGs", fontsize="xx-large",
fontproperties=dict(family="serif"))
# for x in cts_i:
# a_x = gp.dotplot(
# res_pathways_snc_top.loc[x].reset_index(),
# column="Adjusted P-value",
# x="Direction", y="Term Short",
# size=10, top_term=5, figsize=(3, 8),
# title=x, xticklabels_rot=45, yticklabels_rot=45,
# show_ring=True, marker="o", cutoff=0.1)
# a_x.tick_params(axis="y", labelsize=6)
# a_x.tick_params(axis="x", labelsize=10)
# res_pathways_snc_top.loc["Overall"].reset_index(1)[[
# "Gene_set", "Term Short", "Adjusted P-value", "Genes"]].reset_index(
# ).groupby(["Direction", "Gene_set", "Term Short"]).apply(
# lambda x: x.apply(lambda y: y.Genes.split(";"), axis=1),
# include_groups=False).reset_index(-1, drop=True).to_frame(
# "Gene")
res_pathways_snc_top[["Term Short"] + list(
res_pathways_snc_top.columns.difference(["Term", "Term Short"]))]
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms))
Out[102]:
| Term Short | Adjusted P-value | Combined Score | Gene_set | Genes | Odds Ratio | Old Adjusted P-value | Old P-value | Overlap | P-value | level_2 | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| annotation_by_overlap | Direction | ||||||||||||
| Astrocyte | down | 97 | GTEx Ovary 20-29 vs 30-39 Up | 0.000781 | 577.981962 | GTEx_Aging_Signatures_2021 | BCHE;RSPO3;CCN3 | 59.957490 | 0 | 0 | 3/250 | 0.000065 | 152 |
| 98 | GTEx Ovary 20-29 vs 40-49 Up | 0.000781 | 577.981962 | GTEx_Aging_Signatures_2021 | BCHE;RSPO3;CCN3 | 59.957490 | 0 | 0 | 3/250 | 0.000065 | 153 | ||
| 99 | GTEx Brain 20-29 vs 50-59 Down | 0.000781 | 577.981962 | GTEx_Aging_Signatures_2021 | CHRM3;SLC4A10;RSPO3 | 59.957490 | 0 | 0 | 3/250 | 0.000065 | 154 | ||
| 110 | regulation of ion transmembrane transporter activity (GO:0032412) | 0.007965 | 2042.516553 | GO_Biological_Process_2021 | CHRM3;FXYD5 | 215.740541 | 0 | 0 | 2/39 | 0.000077 | 924 | ||
| 103 | Genes Changed In Brain Vs Spinal Cord Derived OPCs From P7 Mice | 0.016494 | 305.164580 | HDSigDB_Mouse_2021 | CHRM3;BCHE;TMEFF2;CCN3;FXYD5 | 30.557209 | 0 | 0 | 5/1517 | 0.000046 | 189 | ||
| 102 | Striatum RNA M44 Module (Floralwhite) | 0.016494 | 797.520726 | HDSigDB_Mouse_2021 | CHRM3;RSPO3;CCN3 | 76.943005 | 0 | 0 | 3/196 | 0.000032 | 188 | ||
| 111 | regulation of integrin biosynthetic process (GO:0045113) | 0.019272 | 5287.722500 | GO_Biological_Process_2021 | TMEFF2 | 832.875000 | 0 | 0 | 1/5 | 0.001749 | 925 | ||
| 112 | regulation of vascular associated smooth muscle contraction (GO:0003056) | 0.019272 | 5287.722500 | GO_Biological_Process_2021 | CHRM3 | 832.875000 | 0 | 0 | 1/5 | 0.001749 | 926 | ||
| 120 | regulation of Wnt signaling pathway, planar cell polarity pathway (GO:2000095) | 0.019272 | 2398.853181 | GO_Biological_Process_2021 | RSPO3 | 416.354167 | 0 | 0 | 1/9 | 0.003146 | 934 | ||
| 119 | adenylate cyclase-inhibiting G protein-coupled acetylcholine receptor signaling pathway (GO:0007... | 0.019272 | 2797.660500 | GO_Biological_Process_2021 | CHRM3 | 475.857143 | 0 | 0 | 1/8 | 0.002797 | 933 | ||
| up | 43 | retinal cone cell development (GO:0046549) | 0.002625 | 162186.518632 | GO_Biological_Process_2021 | RORB | 19994.000000 | 0 | 0 | 1/6 | 0.000300 | 177 | |
| 42 | amacrine cell differentiation (GO:0035881) | 0.002625 | 165840.178437 | GO_Biological_Process_2021 | RORB | 19995.000000 | 0 | 0 | 1/5 | 0.000250 | 176 | ||
| 45 | retinal rod cell development (GO:0046548) | 0.002625 | 159096.448716 | GO_Biological_Process_2021 | RORB | 19993.000000 | 0 | 0 | 1/7 | 0.000350 | 179 | ||
| 44 | retinal cone cell differentiation (GO:0042670) | 0.002625 | 162186.518632 | GO_Biological_Process_2021 | RORB | 19994.000000 | 0 | 0 | 1/6 | 0.000300 | 178 | ||
| 46 | retinal rod cell differentiation (GO:0060221) | 0.002700 | 154056.469240 | GO_Biological_Process_2021 | RORB | 19991.000000 | 0 | 0 | 1/9 | 0.000450 | 180 | ||
| 48 | photoreceptor cell development (GO:0042461) | 0.002812 | 143801.308860 | GO_Biological_Process_2021 | RORB | 19985.000000 | 0 | 0 | 1/15 | 0.000750 | 182 | ||
| 47 | eye photoreceptor cell differentiation (GO:0001754) | 0.002812 | 148283.119629 | GO_Biological_Process_2021 | RORB | 19988.000000 | 0 | 0 | 1/12 | 0.000600 | 181 | ||
| 49 | eye photoreceptor cell development (GO:0042462) | 0.002812 | 143801.308860 | GO_Biological_Process_2021 | RORB | 19985.000000 | 0 | 0 | 1/15 | 0.000750 | 183 | ||
| 68 | Eye Photoreceptor Cell Development (GO:0042462) | 0.003675 | 146675.876168 | GO_Biological_Process_2023 | RORB | 19987.000000 | 0 | 0 | 1/13 | 0.000650 | 208 | ||
| 69 | Photoreceptor Cell Development (GO:0042461) | 0.003675 | 145187.405357 | GO_Biological_Process_2023 | RORB | 19986.000000 | 0 | 0 | 1/14 | 0.000700 | 209 |
Run ToppGene on SnC DEGs¶
In [50]:
# res_deg_4, res_deg_old_sf_v_gc = [i[0] for i in [
# out_edgr_contrasts, out_edgr_con]]
# res_deg_4, res_deg_old_sf_v_gc = [i[(i.adj_p_value < 0.05) & (
# i.abs_log_fc >= 1)].sort_values(
# "abs_log_fc", ascending=False)
# for i in [res_deg_4, res_deg_old_sf_v_gc]]
mks_ctrsts_snc = dict(out_edgr_contrasts_snc_all_top.reset_index(
1).variable.groupby(col_celltype).apply(lambda x: list(x))) # SnC DEGs
results_toppgene_degs = scflow.pp.annotate_by_toppgene(
mks_ctrsts_snc, species=species, min_genes=1,
categories=["Pathway", "PubMed"])
results_toppgene_degs = results_toppgene_degs.join(pd.Series(
mks_ctrsts_snc).to_frame("DEGs").rename_axis("Gene Set"))
results_toppgene_degs = results_toppgene_degs.groupby("Gene Set").apply(
lambda x: x.sort_values("PValue", ascending=True), include_groups=False)
pthwys = dict(results_toppgene_degs.groupby("Gene Set").apply(
lambda x: [i.capitalize() for i in x.Name.unique()]))
print("\n\n".join([f"{k}: {pthwys[k]}" for k in pthwys]))
results_toppgene_degs[["Name", "Symbols", "GenesInTerm", "GenesInQuery",
"GenesInTermInQuery", "PValue"]].reset_index(
2, drop=True)
Astrocyte: ['Reactome_muscarinic_acetylcholine_receptors', 'Reactome_runx1_regulates_transcription_of_genes_involved_in_wnt_signaling', 'Reactome_bicarbonate_transporters', 'Reactome_acetylcholine_regulates_insulin_secretion', 'Reactome_neurotransmitter_clearance', 'Wp_irinotecan_pathway', 'Wp_nephrogenesis', 'Reactome_synthesis_secretion_and_deacylation_of_ghrelin', 'Kegg_medicus_reference_wnt_signaling_modulation_lgr_rspo', 'Kegg_medicus_reference_machr_ca2_apoptotic_pathway', 'Reactome_regulation_of_fzd_by_ubiquitination', 'Kegg_medicus_variant_mutation_caused_aberrant_abeta_to_machr_ca2_apoptotic_pathway']
Out[50]:
| Name | Symbols | GenesInTerm | GenesInQuery | GenesInTermInQuery | PValue | |||
|---|---|---|---|---|---|---|---|---|
| Gene Set | Gene Set | ID | ||||||
| Astrocyte | Astrocyte | M27300 | REACTOME_MUSCARINIC_ACETYLCHOLINE_RECEPTORS | [CHRM3] | 5 | 7 | 1 | 0.002481 |
| MM15006 | REACTOME_MUSCARINIC_ACETYLCHOLINE_RECEPTORS | [CHRM3] | 5 | 7 | 1 | 0.002481 | ||
| M27801 | REACTOME_RUNX1_REGULATES_TRANSCRIPTION_OF_GENES_INVOLVED_IN_WNT_SIGNALING | [RSPO3] | 6 | 7 | 1 | 0.002976 | ||
| M27335 | REACTOME_BICARBONATE_TRANSPORTERS | [SLC4A10] | 10 | 7 | 1 | 0.004956 | ||
| M1825 | REACTOME_ACETYLCHOLINE_REGULATES_INSULIN_SECRETION | [CHRM3] | 10 | 7 | 1 | 0.004956 | ||
| MM15073 | REACTOME_BICARBONATE_TRANSPORTERS | [SLC4A10] | 10 | 7 | 1 | 0.004956 | ||
| M26914 | REACTOME_NEUROTRANSMITTER_CLEARANCE | [BCHE] | 11 | 7 | 1 | 0.005451 | ||
| M39611 | WP_IRINOTECAN_PATHWAY | [BCHE] | 12 | 7 | 1 | 0.005945 | ||
| M40045 | WP_NEPHROGENESIS | [RSPO3] | 18 | 7 | 1 | 0.008906 | ||
| M813 | REACTOME_SYNTHESIS_SECRETION_AND_DEACYLATION_OF_GHRELIN | [BCHE] | 19 | 7 | 1 | 0.009399 | ||
| M47833 | KEGG_MEDICUS_REFERENCE_WNT_SIGNALING_MODULATION_LGR_RSPO | [RSPO3] | 19 | 7 | 1 | 0.009399 | ||
| M47688 | KEGG_MEDICUS_REFERENCE_MACHR_CA2_APOPTOTIC_PATHWAY | [CHRM3] | 20 | 7 | 1 | 0.009891 | ||
| MM15155 | REACTOME_REGULATION_OF_FZD_BY_UBIQUITINATION | [RSPO3] | 20 | 7 | 1 | 0.009891 | ||
| MM15070 | REACTOME_SYNTHESIS_SECRETION_AND_DEACYLATION_OF_GHRELIN | [BCHE] | 20 | 7 | 1 | 0.009891 | ||
| M47689 | KEGG_MEDICUS_VARIANT_MUTATION_CAUSED_ABERRANT_ABETA_TO_MACHR_CA2_APOPTOTIC_PATHWAY | [CHRM3] | 21 | 7 | 1 | 0.010383 | ||
| M27401 | REACTOME_REGULATION_OF_FZD_BY_UBIQUITINATION | [RSPO3] | 21 | 7 | 1 | 0.010383 |
More Specific Contrasts (pyDESeq2)¶
Group¶
In [ ]:
pdata = scflow.tl.create_pseudobulk(
self.rna, [col_sample, col_condition, col_age],
col_celltype, layer="counts", mode="sum")
design = f"~{col_condition} + {col_age} + {col_condition} * {col_age}"
# edgr = pt.tl.EdgeR(pdata, design=design)
pds2 = pt.tl.PyDESeq2(adata=pdata, design=design)
pds2.fit()
key_control, key_treatment = [keys[col_condition][k] for k in [
"key_control", "key_treatment"]]
key_control_cov, key_treatment_cov = [keys[col_age][k] for k in [
"key_control", "key_treatment"]]
sf_old = {col_condition: key_treatment, col_age: key_treatment_cov}
sf_yng = {col_condition: key_treatment, col_age: key_control_cov}
gc_old = {col_condition: key_control, col_age: key_treatment_cov}
gc_yng = {col_condition: key_control, col_age: key_control_cov}
# Spaceflight-Specific Age DEGs
spaceflight_specfic_age_degs = (pds2.cond(**sf_old) - pds2.cond(**sf_yng)) - (
pds2.cond(**gc_old) - pds2.cond(**gc_yng))
interaction_res_df = pds2.test_contrasts(spaceflight_specfic_age_degs)
pds2.plot_volcano(interaction_res_df, log2fc_thresh=0)
# Age-Specific Spaceflight DEGs
age_specfic_sf_degs = (pds2.cond(**sf_old) - pds2.cond(**gc_old)) - (
pds2.cond(**sf_yng) - pds2.cond(**gc_yng))
interaction_res_df2 = pds2.test_contrasts(age_specfic_sf_degs)
pds2.plot_volcano(interaction_res_df2, log2fc_thresh=0)
# Old vs. Young Spaceflight
age_sf_ix = pds2.cond(**sf_old) - pds2.cond(**sf_yng)
interaction_res_df3 = pds2.test_contrasts(age_sf_ix)
pds2.plot_volcano(interaction_res_df3, log2fc_thresh=0)
# Old vs. Young GC
age_gc_ix = pds2.cond(**gc_old) - pds2.cond(**gc_yng)
interaction_res_df4 = pds2.test_contrasts(age_gc_ix)
pds2.plot_volcano(interaction_res_df4, log2fc_thresh=0)
# SF vs. GC Old
cond_old_ix = pds2.cond(**sf_old) - pds2.cond(**gc_old)
interaction_res_df5 = pds2.test_contrasts(cond_old_ix)
pds2.plot_volcano(interaction_res_df5, log2fc_thresh=0)
# SF vs. GC Young
cond_yng_ix = pds2.cond(**sf_yng) - pds2.cond(**gc_yng)
interaction_res_df6 = pds2.test_contrasts(cond_yng_ix)
pds2.plot_volcano(interaction_res_df6, log2fc_thresh=0)
# Compare
thresh_lfc, thresh_p = 1, 0.001
sf_deg_res_df_old = interaction_res_df5[(
interaction_res_df5.adj_p_value < thresh_p) & (
interaction_res_df5.log_fc.abs() >= thresh_lfc)].set_index("variable")
sf_deg_res_df_yng = interaction_res_df6[(
interaction_res_df6.adj_p_value < thresh_p) & (
interaction_res_df6.log_fc.abs() >= thresh_lfc)].set_index("variable")
sf_deg_res = pd.concat([sf_deg_res_df_old, sf_deg_res_df_yng],
keys=["Old", "Young"], names=["Age Group"])
sf_deg_res.loc[:, "log_fc_abs"] = sf_deg_res.log_fc.abs()
sf_deg_res = sf_deg_res.sort_values("log_fc_abs", ascending=False)[[
"log_fc", "adj_p_value"]].unstack(0)
print("SF DEGs in Both Young & Old: ", sf_deg_res.dropna().index.values)
print("\n\nSF DEGs in Just Young", list(sf_deg_res_df_yng.index.difference(
sf_deg_res_df_old.index)), "\n\n")
print("SF DEGs in Just Old", list(sf_deg_res_df_old.index.difference(
sf_deg_res_df_yng.index)), "\n\n")
sf_deg_res.replace(np.nan, "")
# Marker Set Overlap
print("Both: ", marker_gene_sets.apply(
lambda i: set(sf_deg_res.dropna().index).intersection(set(i))).apply(
lambda x: x if len(x) > 0 else np.nan).dropna(), "\n\n")
print("Young: ", marker_gene_sets.apply(
lambda i: sf_deg_res_df_yng.index.difference(
sf_deg_res_df_old.index).intersection(set(i))).apply(
lambda x: x if len(x) > 0 else np.nan).dropna(), "\n\n")
print("Old: ", marker_gene_sets.apply(
lambda i: sf_deg_res_df_old.index.difference(
sf_deg_res_df_yng.index).intersection(set(i))).apply(
lambda x: x if len(x) > 0 else np.nan).dropna(), "\n\n")
SnCs¶
Old¶
In [147]:
# Setup
design = (f"~{col_condition} + Senescent_Cell_Label + "
f"{col_condition} * Senescent_Cell_Label")
pdata2 = scflow.tl.create_pseudobulk(
self.rna[self.rna.obs[col_age] == keys[col_age]["key_treatment"]], [
col_sample, col_condition, "Senescent_Cell_Label"],
"Senescent_Cell_Label_by_Type", layer="counts", mode="sum")
pds2_snc_old = pt.tl.PyDESeq2(adata=pdata2, design=design)
pds2_snc_old.fit()
key_control, key_treatment = [keys[col_condition][k] for k in [
"key_control", "key_treatment"]]
key_control_cov, key_treatment_cov = "Non-Senescent", "Senescent"
sf_sc = {col_condition: key_treatment,
"Senescent_Cell_Label": key_treatment_cov}
sf_ns = {col_condition: key_treatment,
"Senescent_Cell_Label": key_control_cov}
gc_snc = {col_condition: key_control,
"Senescent_Cell_Label": key_treatment_cov}
gc_ns = {col_condition: key_control, "Senescent_Cell_Label": key_control_cov}
# SnC DEGs
res_df_snc = pds2_snc_old.test_contrasts(pds2_snc_old.contrast(
column="Senescent_Cell_Label", baseline=key_control_cov,
group_to_compare=key_treatment_cov))
print(res_df_snc[res_df_snc.adj_p_value < 0.001].head(10))
pds2_snc_old.plot_volcano(res_df_snc, log2fc_thresh=0)
# Spaceflight-Specific SnC DEGs
spaceflight_specfic_snc_degs = (pds2_snc_old.cond(
**sf_sc) - pds2_snc_old.cond(**sf_ns)) - (
pds2_snc_old.cond(**gc_snc) - pds2_snc_old.cond(**gc_ns))
interaction_res_df_snc_sf = pds2_snc_old.test_contrasts(
spaceflight_specfic_snc_degs)
print(interaction_res_df_snc_sf[
interaction_res_df_snc_sf.adj_p_value < 0.001].head(10))
pds2_snc_old.plot_volcano(interaction_res_df_snc_sf, log2fc_thresh=0)
Fitting size factors... ... done in 0.02 seconds.
Using None as control genes, passed at DeseqDataSet initialization
Fitting dispersions... ... done in 0.70 seconds. Fitting dispersion trend curve... ... done in 0.08 seconds. Fitting MAP dispersions... ... done in 0.64 seconds. Fitting LFCs... ... done in 0.64 seconds. Calculating cook's distance... ... done in 0.03 seconds. Replacing 37 outlier genes. Fitting dispersions... ... done in 0.03 seconds. Fitting MAP dispersions... ... done in 0.02 seconds. Fitting LFCs... ... done in 0.02 seconds. Running Wald tests... ... done in 0.42 seconds.
Log2 fold change & Wald test p-value, contrast vector: [0. 0. 1. 0.]
baseMean log2FoldChange lfcSE stat pvalue padj
Xkr4 179.212479 -0.011190 0.508024 -0.022026 0.982427 0.995695
Gm37180 2.730003 0.081640 0.653310 0.124964 0.900552 0.966170
Gm37363 1.127911 0.481627 0.816977 0.589523 0.555511 0.794140
Gm1992 2.308152 0.845376 0.665170 1.270916 0.203759 0.486743
Gm38148 1.674510 0.250140 0.745442 0.335559 0.737203 0.892765
... ... ... ... ... ... ...
Hccs 2.337087 0.156308 0.384872 0.406130 0.684647 0.867528
Gm15246 2.716744 0.006002 0.398421 0.015064 0.987981 0.996809
Mid1 8.839969 -0.285549 0.538941 -0.529833 0.596228 0.816407
Gm47283 15.537917 -0.055943 0.273288 -0.204705 0.837803 0.939912
ENSMUSG00000095041 130.039870 0.076967 0.283340 0.271642 0.785897 0.915620
[12097 rows x 6 columns]
variable baseMean log_fc lfcSE stat p_value adj_p_value contrast
0 Ctnnb1 21.824530 1.817093 0.228152 7.964405 1.660204e-15 2.008349e-11 None
1 Nap1l4 15.838713 1.323793 0.175228 7.554687 4.198689e-14 2.539577e-10 None
2 Plat 6.809091 2.354012 0.319697 7.363266 1.794638e-13 7.236577e-10 None
3 Bex3 15.928398 2.256285 0.338853 6.658591 2.764655e-11 8.361009e-08 None
4 Mif 20.886258 1.914521 0.290979 6.579594 4.717338e-11 1.141313e-07 None
5 Hmgb1 23.221856 1.335376 0.210520 6.343220 2.250111e-10 4.536599e-07 None
6 Egf 2.771015 1.916097 0.326017 5.877295 4.170257e-09 7.206800e-06 None
7 Igfbp6 7.040783 3.186753 0.560197 5.688629 1.280632e-08 1.787173e-05 None
8 Igfbp4 4.873177 2.807310 0.494052 5.682213 1.329632e-08 1.787173e-05 None
9 Ctsb 57.118812 1.427001 0.254592 5.605056 2.081874e-08 2.518443e-05 None
Running Wald tests... ... done in 0.43 seconds.
Log2 fold change & Wald test p-value, contrast vector: [0. 0. 0. 1.]
baseMean log2FoldChange lfcSE stat pvalue padj
Xkr4 179.212479 -0.463716 0.683672 -0.678272 0.497599 0.999947
Gm37180 2.730003 0.186882 0.877935 0.212865 0.831432 0.999947
Gm37363 1.127911 0.301098 1.085249 0.277446 0.781438 0.999947
Gm1992 2.308152 -0.713486 0.912579 -0.781835 0.434312 0.999947
Gm38148 1.674510 0.330969 0.991846 0.333691 0.738613 0.999947
... ... ... ... ... ... ...
Hccs 2.337087 -0.044311 0.545358 -0.081251 0.935242 0.999947
Gm15246 2.716744 -0.136966 0.579655 -0.236290 0.813208 0.999947
Mid1 8.839969 0.059856 0.729548 0.082046 0.934610 0.999947
Gm47283 15.537917 0.289462 0.369284 0.783846 0.433130 0.999947
ENSMUSG00000095041 130.039870 -0.007400 0.381221 -0.019410 0.984514 0.999947
[12097 rows x 6 columns]
Empty DataFrame
Columns: [variable, baseMean, log_fc, lfcSE, stat, p_value, adj_p_value, contrast]
Index: []
Young¶
In [145]:
# Setup
design = (f"~{col_condition} + Senescent_Cell_Label + "
f"{col_condition} * Senescent_Cell_Label")
pdata2 = scflow.tl.create_pseudobulk(
self.rna[self.rna.obs[col_age] == keys[col_age]["key_treatment"]], [
col_sample, col_condition, "Senescent_Cell_Label"],
"Senescent_Cell_Label_by_Type", layer="counts", mode="sum")
pds2_snc_yng = pt.tl.PyDESeq2(adata=pdata2, design=design)
pds2_snc_yng.fit()
key_control, key_treatment = [keys[col_condition][k] for k in [
"key_control", "key_treatment"]]
key_control_cov, key_treatment_cov = "Non-Senescent", "Senescent"
sf_sc = {col_condition: key_treatment,
"Senescent_Cell_Label": key_treatment_cov}
sf_ns = {col_condition: key_treatment,
"Senescent_Cell_Label": key_control_cov}
gc_snc = {col_condition: key_control,
"Senescent_Cell_Label": key_treatment_cov}
gc_ns = {col_condition: key_control, "Senescent_Cell_Label": key_control_cov}
# SnC DEGs
res_df_snc = pds2_snc_yng.test_contrasts(pds2_snc_yng.contrast(
column="Senescent_Cell_Label", baseline=key_control_cov,
group_to_compare=key_treatment_cov))
print(res_df_snc[res_df_snc.adj_p_value < 0.001].head(10))
pds2_snc_yng.plot_volcano(res_df_snc, log2fc_thresh=0)
# Spaceflight-Specific SnC DEGs
spaceflight_specfic_snc_degs = (pds2_snc_yng.cond(
**sf_sc) - pds2_snc_yng.cond(**sf_ns)) - (
pds2_snc_yng.cond(**gc_snc) - pds2_snc_yng.cond(**gc_ns))
interaction_res_df_snc_sf = pds2_snc_yng.test_contrasts(
spaceflight_specfic_snc_degs)
pds2_snc_yng.plot_volcano(interaction_res_df_snc_sf, log2fc_thresh=0)
Fitting size factors... ... done in 0.02 seconds.
Using None as control genes, passed at DeseqDataSet initialization
Fitting dispersions... ... done in 0.72 seconds. Fitting dispersion trend curve... ... done in 0.08 seconds. Fitting MAP dispersions... ... done in 0.65 seconds. Fitting LFCs... ... done in 1.81 seconds. Calculating cook's distance... ... done in 0.03 seconds. Replacing 37 outlier genes. Fitting dispersions... ... done in 0.02 seconds. Fitting MAP dispersions... ... done in 0.02 seconds. Fitting LFCs... ... done in 0.02 seconds. Running Wald tests... ... done in 0.47 seconds.
Log2 fold change & Wald test p-value, contrast vector: [0. 0. 1. 0.]
baseMean log2FoldChange lfcSE stat pvalue padj
Xkr4 179.212479 -0.011190 0.508024 -0.022026 0.982427 0.995695
Gm37180 2.730003 0.081640 0.653310 0.124964 0.900552 0.966170
Gm37363 1.127911 0.481627 0.816977 0.589523 0.555511 0.794140
Gm1992 2.308152 0.845376 0.665170 1.270916 0.203759 0.486743
Gm38148 1.674510 0.250140 0.745442 0.335559 0.737203 0.892765
... ... ... ... ... ... ...
Hccs 2.337087 0.156308 0.384872 0.406130 0.684647 0.867528
Gm15246 2.716744 0.006002 0.398421 0.015064 0.987981 0.996809
Mid1 8.839969 -0.285549 0.538941 -0.529833 0.596228 0.816407
Gm47283 15.537917 -0.055943 0.273288 -0.204705 0.837803 0.939912
ENSMUSG00000095041 130.039870 0.076967 0.283340 0.271642 0.785897 0.915620
[12097 rows x 6 columns]
variable baseMean log_fc lfcSE stat p_value adj_p_value contrast
0 Ctnnb1 21.824530 1.817093 0.228152 7.964405 1.660204e-15 2.008349e-11 None
1 Nap1l4 15.838713 1.323793 0.175228 7.554687 4.198689e-14 2.539577e-10 None
2 Plat 6.809091 2.354012 0.319697 7.363266 1.794638e-13 7.236577e-10 None
3 Bex3 15.928398 2.256285 0.338853 6.658591 2.764655e-11 8.361009e-08 None
4 Mif 20.886258 1.914521 0.290979 6.579594 4.717338e-11 1.141313e-07 None
5 Hmgb1 23.221856 1.335376 0.210520 6.343220 2.250111e-10 4.536599e-07 None
6 Egf 2.771015 1.916097 0.326017 5.877295 4.170257e-09 7.206800e-06 None
7 Igfbp6 7.040783 3.186753 0.560197 5.688629 1.280632e-08 1.787173e-05 None
8 Igfbp4 4.873177 2.807310 0.494052 5.682213 1.329632e-08 1.787173e-05 None
9 Ctsb 57.118812 1.427001 0.254592 5.605056 2.081874e-08 2.518443e-05 None
Running Wald tests... ... done in 0.43 seconds.
Log2 fold change & Wald test p-value, contrast vector: [0. 0. 0. 1.]
baseMean log2FoldChange lfcSE stat pvalue padj
Xkr4 179.212479 -0.463716 0.683672 -0.678272 0.497599 0.999947
Gm37180 2.730003 0.186882 0.877935 0.212865 0.831432 0.999947
Gm37363 1.127911 0.301098 1.085249 0.277446 0.781438 0.999947
Gm1992 2.308152 -0.713486 0.912579 -0.781835 0.434312 0.999947
Gm38148 1.674510 0.330969 0.991846 0.333691 0.738613 0.999947
... ... ... ... ... ... ...
Hccs 2.337087 -0.044311 0.545358 -0.081251 0.935242 0.999947
Gm15246 2.716744 -0.136966 0.579655 -0.236290 0.813208 0.999947
Mid1 8.839969 0.059856 0.729548 0.082046 0.934610 0.999947
Gm47283 15.537917 0.289462 0.369284 0.783846 0.433130 0.999947
ENSMUSG00000095041 130.039870 -0.007400 0.381221 -0.019410 0.984514 0.999947
[12097 rows x 6 columns]
SnCs (More Permissive Threshold)¶
In [150]:
# Compare Less & More Permissive Thresholds
print("Less & More Permissive SnC Threshold: ", self.rna.obs[[
"Senescent_Cell_Label", f"Senescent_Cell_Label_{use_metric}_{p_h}"
]].value_counts())
sen_lab = f"Senescent_Cell_Label_{use_metric}_{p_h}"
# Setup
design = (f"~{col_condition} + {sen_lab} + "
f"{col_condition} * {sen_lab}")
pdata2_3 = scflow.tl.create_pseudobulk(
self.rna[self.rna.obs[col_age] == keys[col_age]["key_treatment"]], [
col_sample, col_condition, sen_lab],
col_celltype, layer="counts", mode="sum")
pds2_snc_old_3 = pt.tl.PyDESeq2(adata=pdata2_3, design=design)
pds2_snc_old_3.fit()
key_control, key_treatment = [keys[col_condition][k] for k in [
"key_control", "key_treatment"]]
key_control_cov, key_treatment_cov = "Non-Senescent", "Senescent"
sf_sc = {col_condition: key_treatment, sen_lab: key_treatment_cov}
sf_ns = {col_condition: key_treatment, sen_lab: key_control_cov}
gc_snc = {col_condition: key_control, sen_lab: key_treatment_cov}
gc_ns = {col_condition: key_control, sen_lab: key_control_cov}
# SnC DEGs
res_df_snc_3 = pds2_snc_old_3.test_contrasts(pds2_snc_old_3.contrast(
column=sen_lab, baseline=key_control_cov,
group_to_compare=key_treatment_cov))
print(res_df_snc_3[res_df_snc_3.adj_p_value < 0.001].head(10))
pds2_snc_old_3.plot_volcano(res_df_snc_3, log2fc_thresh=0)
# Spaceflight-Specific SnC DEGs
spaceflight_specfic_snc_degs_3 = (pds2_snc_old_3.cond(
**sf_sc) - pds2_snc_old_3.cond(**sf_ns)) - (
pds2_snc_old_3.cond(**gc_snc) - pds2_snc_old_3.cond(**gc_ns))
interaction_res_df_snc_sf_3 = pds2_snc_old_3.test_contrasts(
spaceflight_specfic_snc_degs_3)
pds2_snc_old_3.plot_volcano(interaction_res_df_snc_sf_3, log2fc_thresh=0)
Less & More Permissive SnC Threshold: Senescent_Cell_Label Senescent_Cell_Label_senmayo_3
Non-Senescent Non-Senescent 98751
Senescent 2906
Senescent Senescent 1617
Name: count, dtype: int64
Fitting size factors... ... done in 0.02 seconds.
Using None as control genes, passed at DeseqDataSet initialization
Fitting dispersions... ... done in 0.72 seconds. Fitting dispersion trend curve... ... done in 0.08 seconds. Fitting MAP dispersions... ... done in 0.67 seconds. Fitting LFCs... ... done in 0.63 seconds. Calculating cook's distance... ... done in 0.03 seconds. Replacing 9 outlier genes. Fitting dispersions... ... done in 0.01 seconds. Fitting MAP dispersions... ... done in 0.01 seconds. Fitting LFCs... ... done in 0.01 seconds. Running Wald tests... ... done in 0.45 seconds.
Log2 fold change & Wald test p-value, contrast vector: [0. 0. 1. 0.]
baseMean log2FoldChange lfcSE stat pvalue padj
Xkr4 206.366237 -0.314513 0.460150 -0.683501 0.494290 0.855347
Gm37180 3.295234 -0.110652 0.517918 -0.213647 0.830822 0.967382
Gm37363 1.461366 0.357581 0.628605 0.568848 0.569459 0.889360
Gm1992 2.968704 0.330792 0.537530 0.615393 0.538295 0.876403
Gm38148 2.144230 0.319264 0.563897 0.566174 0.571276 0.889653
... ... ... ... ... ... ...
Hccs 3.883770 -0.121759 0.278209 -0.437653 0.661638 0.923309
Gm15246 4.511894 -0.207823 0.285125 -0.728883 0.466073 0.840147
Mid1 15.125835 -0.211798 0.501828 -0.422052 0.672987 0.927130
Gm47283 19.752385 0.003665 0.187004 0.019600 0.984362 0.996717
ENSMUSG00000095041 169.351949 0.073776 0.218794 0.337192 0.735972 0.946349
[12097 rows x 6 columns]
variable baseMean log_fc lfcSE stat p_value adj_p_value contrast
0 Ctnnb1 25.875327 1.448020 0.157995 9.164952 4.956885e-20 5.996343e-16 None
1 Nap1l4 21.637783 1.072293 0.141512 7.577385 3.525899e-14 2.132640e-10 None
2 Pigf 6.112808 1.415157 0.189655 7.461751 8.538032e-14 3.095653e-10 None
3 Hmgb1 27.509007 1.337570 0.179834 7.437819 1.023610e-13 3.095653e-10 None
4 Egf 3.539228 1.574905 0.227195 6.931958 4.150563e-12 1.004187e-08 None
5 Tubgcp2 9.734000 1.174394 0.175279 6.700138 2.082226e-11 4.198116e-08 None
6 Ptbp1 6.105108 1.601140 0.243989 6.562347 5.296738e-11 9.153519e-08 None
7 Igfbp4 6.783021 2.903719 0.448717 6.471161 9.725286e-11 1.470585e-07 None
8 Mif 25.478212 1.663531 0.268315 6.199924 5.649059e-10 7.286883e-07 None
9 Plat 7.247313 1.827339 0.295217 6.189809 6.023711e-10 7.286883e-07 None
Running Wald tests... ... done in 0.46 seconds.
Log2 fold change & Wald test p-value, contrast vector: [0. 0. 0. 1.]
baseMean log2FoldChange lfcSE stat pvalue padj
Xkr4 206.366237 -0.063479 0.619425 -0.102480 0.918376 0.999955
Gm37180 3.295234 -0.018667 0.697859 -0.026749 0.978660 0.999955
Gm37363 1.461366 -0.147345 0.847134 -0.173934 0.861918 0.999955
Gm1992 2.968704 -0.211352 0.716743 -0.294878 0.768087 0.999955
Gm38148 2.144230 -0.352492 0.767315 -0.459383 0.645959 0.999955
... ... ... ... ... ... ...
Hccs 3.883770 0.232654 0.370701 0.627605 0.530262 0.999955
Gm15246 4.511894 -0.077720 0.397181 -0.195679 0.844861 0.999955
Mid1 15.125835 0.220451 0.674257 0.326954 0.743702 0.999955
Gm47283 19.752385 0.147430 0.251960 0.585133 0.558458 0.999955
ENSMUSG00000095041 169.351949 0.053483 0.294483 0.181615 0.855885 0.999955
[12097 rows x 6 columns]
SnC GEX Profiles¶
SnC DEGs by Group¶
In [192]:
top_tx_g_snc = res_snc_grps.loc[keys[col_condition][
"key_treatment"]].sort_values("abs_log_fc", ascending=False).index
print(res_snc_grps.unstack(0).replace(np.nan, "")[[
"log_fc", "adj_p_value"]].loc[top_tx_g_snc])
just_sf = res_snc_grps.loc[keys[col_condition][
"key_treatment"]].index.difference(res_snc_grps.loc[keys[
col_condition]["key_control"]].index)
just_gc = res_snc_grps.loc[keys[col_condition][
"key_control"]].index.difference(res_snc_grps.loc[keys[
col_condition]["key_treatment"]].index)
print(res_snc_grps.loc[:, just_gc, :].sort_values(
"abs_log_fc", ascending=False)[["log_fc", "adj_p_value"]], "\n\n")
res_snc_grps.loc[:, just_sf, :].sort_values(
"abs_log_fc", ascending=False)[["log_fc", "adj_p_value"]]
self.plot(kind=["matrix"], genes=just_sf, col_celltype=col_batch,
matrix=dict(swap_axes=True, standard_scale="var"))
log_fc adj_p_value
Condition Ground Control Space Flight Ground Control Space Flight
variable
Vip 3.156433 4.289319 0.0 0.0
Slc39a12 -3.551992 -3.180457 0.000003 0.000004
Tac2 1.921187 3.066049 0.000006 0.0
Phkg1 -2.968877 0.000006
Acsbg1 -2.952229 0.000003
Nwd1 -3.290416 -2.880781 0.000002 0.000008
Nhsl1 -3.295761 -2.7655 0.0 0.000002
Lrig1 -2.744472 0.0
Adhfe1 -2.726487 0.000007
Ptgs2 2.672912 0.000003
Acss1 -2.24669 -2.55111 0.000006 0.000001
Pnpla7 -1.995559 -2.518648 0.000006 0.0
Irak2 -2.447392 0.000003
Sema4b -2.344446 0.000001
Myo10 -2.299259 0.000001
Fosb 2.253945 0.000001
4930488L21Rik -2.172608 -2.194716 0.000001 0.000001
Gpld1 -2.172901 0.000002
Paqr8 -2.584717 -2.076295 0.0 0.0
Rorb -2.093526 -2.038129 0.000002 0.000005
Crot -1.772943 -2.028285 0.0 0.0
Rcbtb2 -2.024693 0.000006
Trp53bp2 -2.018176 0.000003
Phka1 -1.800492 -2.011669 0.000001 0.000001
Tspan9 -1.543476 -1.98319 0.000007 0.0
Sat1 -1.97296 0.000002
Cables1 -1.95444 0.000001
Abhd3 -1.71809 -1.922764 0.000008 0.0
Pag1 -1.917645 0.000007
Egr4 1.907752 0.000001
Soat1 -1.870687 0.0
Junos -1.869604 0.000008
Limk1 -1.867847 0.000001
Appl2 -2.20817 -1.862894 0.0 0.0
Psd2 -1.845588 0.00001
Slc27a1 -1.727418 -1.82927 0.000004 0.000006
Gpam -1.826651 0.000004
Pitpnc1 -1.873934 -1.787914 0.000001 0.000002
Fam228b -1.881222 -1.76545 0.0 0.000003
Cntrl -1.584117 -1.761886 0.000001 0.0
Crlf3 -1.755764 0.000002
Vgf 1.674676 1.753261 0.000001 0.000001
Hacd2 -1.731295 -1.739704 0.0 0.0
Rbm12 -1.699022 0.000002
Rft1 -1.694305 0.000006
Rnls -1.685472 0.000004
St3gal4 -1.90577 -1.683557 0.000001 0.000005
Mitf -1.558499 -1.679271 0.0 0.0
Agl -1.720674 -1.667137 0.0 0.000001
Eps15 -1.664543 0.0
Slc10a7 -1.256906 -1.659341 0.000009 0.0
Aff1 -1.657693 0.000008
Cyfip1 -1.228227 -1.651509 0.000008 0.0
Enox2 -1.452157 -1.643267 0.000001 0.0
Igfbp4 1.74288 1.640399 0.000005 0.000003
Sfxn5 -1.894613 -1.624192 0.0 0.000009
Lims1 -1.149641 -1.619867 0.000008 0.0
Nek7 -1.619015 0.000001
Glud1 -1.607609 -1.614271 0.0 0.0
Stard9 -1.589535 -1.611922 0.000004 0.000001
Nfia -1.589755 -1.611206 0.000005 0.000004
Gk -1.552192 -1.600816 0.0 0.000001
Golim4 -1.594119 0.000007
Denn2b -1.58493 0.000009
Gna13 -1.648417 -1.576397 0.000001 0.000003
Adk -1.547145 -1.5588 0.0 0.0
Dleu2 -1.32858 -1.554745 0.000005 0.000003
Etv6 -1.517564 -1.548585 0.0 0.0
Gm16759 -1.603156 -1.532369 0.000001 0.000001
Cep192 -1.53033 0.0
Atp7a -1.490834 0.000003
Ppcdc -1.490009 0.000004
Pepd -1.481809 0.000002
Arhgef1 -1.479845 0.000001
Farp1 -1.751982 -1.471499 0.0 0.000001
Dram2 -1.469943 0.000001
Hdac1 -1.46536 0.000001
Afg1l -1.462639 0.0
Hdac8 -1.656257 -1.461528 0.0 0.0
Msi2 -1.384742 -1.459267 0.000004 0.0
Zfp652 -1.45775 0.0
Osbpl11 -1.47349 -1.456132 0.000002 0.000003
Tank -1.212401 -1.455025 0.000001 0.0
Ddhd1 -1.715886 -1.45401 0.0 0.000001
Aven -1.452047 0.000004
Elovl5 -1.451515 0.000007
Foxn2 -1.448836 0.000008
4931406C07Rik -1.448177 0.000005
Wdpcp -1.443782 0.0
Mthfs -1.425196 -1.443521 0.000005 0.000001
Frmd4a -1.553984 -1.441329 0.000003 0.000003
Gng12 -1.470624 -1.432209 0.000008 0.00001
Hivep3 -1.442951 -1.429465 0.000001 0.000002
Usp6nl -1.399203 -1.42689 0.000006 0.000007
Mlxip -1.416183 0.000003
2610005L07Rik -1.416047 0.000001
Ift140 -1.415683 0.000003
Pcyt1b -1.414975 0.000008
Fkbp15 -1.408662 0.0
E2f3 -1.395828 0.000005
Fggy -1.441128 -1.395322 0.000001 0.000001
Rttn -1.415685 -1.391445 0.000001 0.000003
Bbs9 -1.377205 0.0
Fhit -1.208899 -1.376201 0.000005 0.000007
Trps1 -1.504695 -1.373787 0.0 0.000002
Kdm3b -1.372672 0.0
Anks1 -1.371977 0.0
Wdfy2 -1.587633 -1.371931 0.0 0.000002
Ptprm -1.367174 0.000001
Fmn1 -1.357201 0.000001
Slc30a7 -1.356308 0.0
2700049A03Rik -1.354757 0.000001
Phactr2 -1.373819 -1.353479 0.000009 0.000007
Hibch -1.352369 0.000003
Pbx1 -1.372138 -1.350707 0.000003 0.0
Abcb7 -1.34958 0.0
Parp8 -1.096418 -1.346572 0.000003 0.0
Gab2 -1.421926 -1.34278 0.0 0.000003
Foxn3 -1.124782 -1.340366 0.000009 0.000001
Rap1a -1.339492 0.000003
Zbtb20 -1.333404 0.000005
Tubgcp5 -1.33181 0.000002
Ranbp10 -1.331549 0.0
Macf1 -1.361563 -1.331407 0.0 0.0
Xpnpep1 -1.330197 0.000001
Mettl15 -1.329575 0.000005
Metap1d -1.328115 0.000002
Phkb -1.551834 -1.327937 0.0 0.0
Mir100hg -1.141413 -1.324218 0.000006 0.000001
Dock4 -1.210086 -1.321161 0.000001 0.0
Arhgap12 -1.318575 0.000001
Bckdhb -1.257716 -1.317004 0.0 0.0
Kiz -1.313942 0.000004
Zfp182 -1.127409 -1.306316 0.000007 0.000001
Snx9 -1.293759 -1.305376 0.000001 0.000003
Atm -1.458323 -1.305022 0.000003 0.0
Mir99ahg -1.338685 -1.303796 0.0 0.000001
Arhgap39 -1.194132 -1.30198 0.00001 0.000001
Marf1 -1.301696 0.000001
Exoc2 -1.2993 0.0
Usp24 -1.479372 -1.298471 0.0 0.000002
Ikbkb -1.296711 0.000005
Chchd3 -1.212952 -1.296071 0.0 0.0
Iffo1 -1.292995 0.000003
Spire1 -1.481037 -1.289635 0.0 0.0
Ets2 1.288177 0.000008
1110019D14Rik -1.287858 0.00001
Ssh2 -1.431823 -1.281648 0.0 0.00001
Fars2 -1.233951 -1.280203 0.0 0.0
Zfp609 -1.277499 0.0
Rbmx -1.275619 0.000009
Rsu1 -1.274831 0.000001
Ngly1 -1.266358 0.000009
Tmem260 -1.358448 -1.261991 0.000001 0.0
Mtmr3 -1.141664 -1.258729 0.0 0.0
Zhx3 -1.257531 0.000003
Pvt1 -1.256151 0.000009
Clint1 -1.254483 0.0
Pou2f1 -1.333633 -1.254153 0.0 0.0
Ksr1 -1.253239 0.000001
Dip2b -1.209215 -1.252458 0.000002 0.000001
St3gal3 -1.180196 -1.249606 0.000006 0.0
Pphln1 -1.734731 -1.24902 0.0 0.000007
Ints6l -1.248422 0.000003
Wrn -1.248163 0.000003
Aatf -1.246979 0.000001
Map4k4 -1.279851 -1.245586 0.0 0.000001
Peak1 -1.242027 0.000003
Asph -1.241851 0.000008
Tbl1xr1 -1.220068 -1.241487 0.0 0.0
5430405H02Rik -1.240385 0.000009
Garre1 -1.240103 0.000002
Acer3 -1.208879 -1.238302 0.000001 0.000002
Agps -1.237916 0.0
Ide -1.235851 0.0
Mbnl1 -1.408941 -1.235766 0.0 0.000009
5830417I10Rik -1.230097 0.000006
Caap1 -1.229411 0.0
Ccdc88a -1.389542 -1.22524 0.0 0.0
Cdin1 -1.222015 0.000004
Srbd1 -1.221812 0.000008
Nav2 -1.218302 0.000001
Snx29 -1.308683 -1.217001 0.000002 0.000008
Cep85l -1.603023 -1.214891 0.0 0.00001
Gpatch2l -1.214858 0.000002
Mdm4 -1.002103 -1.214431 0.000002 0.0
Nfkb1 -1.095584 -1.214374 0.000002 0.0
Vps35l -1.227198 -1.214295 0.000002 0.0
Herc4 -1.174051 -1.212011 0.0 0.000002
Adam10 -1.205012 0.0
Arhgap5 -1.527251 -1.200615 0.0 0.000005
Zfp532 -1.125011 -1.200488 0.000001 0.000002
Gpatch2 -1.200008 0.0
Naa16 -1.193444 -1.199055 0.000003 0.000002
Mkln1 -1.084343 -1.196997 0.0 0.0
Dst -1.075446 -1.195719 0.0 0.0
Fmnl2 -1.250118 -1.195355 0.000001 0.000004
Abl1 -1.192791 0.00001
Taf15 -1.114306 -1.191868 0.0 0.0
Zfp326 -1.191233 0.000002
Ufl1 -1.188672 0.0
Baz2b -1.000806 -1.1882 0.000009 0.0
Mvb12b -1.431369 -1.18731 0.0 0.000001
Arhgap17 -1.187129 0.000009
Lamp2 -1.094012 -1.186495 0.000005 0.000002
Srsf4 -1.186111 0.000002
Zfand6 -1.185857 0.000003
Cdkal1 -1.085924 -1.184648 0.0 0.0
Dennd4c -1.181313 0.000009
Pias1 -1.040821 -1.179422 0.000001 0.0
Myo18a -1.236738 -1.178149 0.0 0.000002
Fam222b -1.103829 -1.176324 0.000008 0.0
Nfyc -1.174726 0.000001
Bex3 1.353308 1.173545 0.0 0.000001
5031425E22Rik -1.173343 0.0
Rspry1 -1.143806 -1.170803 0.000003 0.0
Apbb2 -1.228567 -1.169639 0.0 0.000004
Kdm3a -1.169541 0.000001
Fbxl12 -1.169216 0.00001
Urgcp -1.168905 0.000001
Raf1 -1.167525 0.000001
Armh3 -1.16686 0.000001
Zfyve1 -1.165307 0.000003
Ccdc58 -1.164089 0.000004
Pik3c2a -1.024146 -1.163565 0.000003 0.0
Mapk14 -1.155314 0.000001
Wwox -1.071801 -1.15357 0.0 0.0
Lrrfip2 -1.152523 0.000001
Zfp608 -1.135659 -1.151419 0.000001 0.000002
Gpbp1l1 -1.150877 0.000001
Cdc27 -1.150376 0.000001
Arid4a -1.150133 0.000001
Rap1gds1 -1.118123 -1.149898 0.000002 0.0
Ptpn9 -1.148738 0.000001
Ralgapb -1.148341 0.000001
Trim24 -1.146034 0.000001
Wnk1 -1.231969 -1.143889 0.0 0.000008
Chd9 -1.227761 -1.143509 0.0 0.0
Acaca -1.286029 -1.143042 0.0 0.000005
Akap10 -1.014584 -1.142386 0.000003 0.0
Map3k3 -1.012288 -1.141479 0.000004 0.0
Rock1 -1.139758 0.0
Klf7 -1.119801 -1.137407 0.000001 0.0
Tbcel -1.137367 0.000007
Zfp407 -1.136468 0.000001
Cog5 -1.135283 0.0
Chaserr -1.13249 0.0
Itch -1.280323 -1.132281 0.0 0.0
Dennd4a -1.125418 -1.132054 0.000002 0.000003
Pwwp2a -1.13097 0.000001
Pkd2 -1.130946 0.000003
Camkmt -1.118409 -1.12973 0.000001 0.000001
Tdrd3 -1.000823 -1.128264 0.000008 0.0
Clasp2 -1.243668 -1.127981 0.0 0.0
Rb1 -1.280945 -1.12728 0.0 0.000001
Tbc1d9b -1.125478 0.000007
Wwp1 -1.125445 0.000002
Diaph2 -1.124863 0.0
Prkn -1.153864 -1.123061 0.0 0.0
Phf21a -1.029126 -1.122967 0.000001 0.0
Prdm2 -1.121082 0.000001
Immp2l -1.119868 0.0
Exoc4 -1.119411 0.0
Nt5c2 -1.118587 0.000001
Setd2 -1.117 0.0
Fus -1.116371 0.0
Mia2 -1.116298 0.000001
Fcho2 -1.115583 0.000001
Orc3 -1.112079 0.000002
Rsrp1 -1.111908 0.0
Kat6b -1.074353 -1.111771 0.000002 0.000001
Ralgps1 -1.111629 0.000004
Sh3kbp1 -1.110383 0.00001
Mtm1 -1.167992 -1.109693 0.000008 0.000005
Tmem135 -1.054575 -1.109354 0.0 0.0
Kansl1l -1.055743 -1.109265 0.0 0.0
Gbf1 -1.107221 0.0
Sh3glb1 -1.106899 0.000001
Pbrm1 -1.106482 0.0
Sptlc2 -1.106367 0.000002
Cep170 -1.023061 -1.106086 0.0 0.000001
Ptar1 -1.105347 0.000006
Map3k2 -1.064533 -1.104862 0.0 0.0
Strada -1.102824 0.000004
Dnaaf9 -1.36844 -1.102603 0.0 0.000002
Specc1 -1.167122 -1.102403 0.0 0.0
Lin52 -1.102008 0.000004
Kansl1 -1.034334 -1.098365 0.0 0.0
Abi1 -1.249959 -1.095722 0.0 0.000001
Tnrc6b -1.089517 -1.09466 0.0 0.0
Dido1 -1.093208 0.000004
Mon2 -1.09268 0.000002
AU040320 -1.09205 0.000003
Cyb5r4 -1.091985 0.000004
Hectd1 -1.09076 0.0
Mfn1 -1.318819 -1.090036 0.0 0.000007
Cnnm2 -1.088328 0.00001
Spata5 -1.087306 0.000003
Dcaf8 -1.086865 0.0
Supt3 -1.086731 0.000002
Med13l -1.086676 0.0
Esco1 -1.08606 0.0
Stx8 -1.084876 0.000001
Ddx17 -1.080572 -1.082997 0.000001 0.0
Atad2b -1.212455 -1.08135 0.0 0.0
Dpy19l4 -1.077825 0.000003
Aftph -1.076983 0.000001
Dcp1b -1.073342 0.000008
Tsix -1.065956 -1.072892 0.000003 0.000001
Xist -1.077449 -1.072606 0.0 0.0
Epc1 -1.071946 -1.072105 0.0 0.000001
Atf7ip -1.150905 -1.071788 0.0 0.000002
Cnot4 -1.071681 0.0
Rfx3 -1.071429 0.000007
Zup1 -1.071179 0.000003
Cspp1 -1.079079 -1.070968 0.000002 0.000002
Ppip5k2 -1.070868 0.000007
Fbxl20 -1.070577 0.0
Cpeb4 -1.155114 -1.069591 0.0 0.000002
Zfp277 -1.06933 0.000009
Ehbp1 -1.160652 -1.069179 0.0 0.000001
Eri3 -1.069108 0.000008
Gm21992 -1.068347 0.000004
Mllt10 -1.001423 -1.067855 0.0 0.0
Usp8 -1.066508 0.000008
Hmg20a -1.153541 -1.066058 0.000001 0.0
Smc5 -1.160576 -1.065999 0.000005 0.000003
Ap3b1 -1.080438 -1.061902 0.000001 0.000001
Reps1 -1.061845 0.000005
Nsmce2 -1.147576 -1.061745 0.0 0.0
Exoc6b -1.143799 -1.061083 0.000001 0.000004
Vps54 -1.0609 0.0
Fnbp4 -1.060448 0.000001
Zfc3h1 -1.060356 0.0
Phc3 -1.060246 0.0
Mier1 -1.02861 -1.059474 0.000001 0.0
Pik3ca -1.057906 0.000001
Akt3 -1.058985 -1.057594 0.000003 0.000001
Cd2ap -1.054839 0.0
Armc8 -1.033047 -1.054456 0.000009 0.000001
Tab2 -1.118642 -1.052307 0.000001 0.000004
Vti1a -1.085728 -1.05173 0.0 0.0
D130040H23Rik -1.200104 -1.051713 0.000007 0.000009
Tasor2 -1.051641 0.000002
Copg2 -1.051164 0.000003
Gnaq -1.211268 -1.051124 0.0 0.0
Tut7 -1.050976 0.000001
Stk38 -1.050363 0.000003
Ambra1 -1.038843 -1.050164 0.0 0.0
Spopl -1.041162 -1.049901 0.000008 0.000002
Ktn1 -1.093128 -1.04837 0.0 0.0
Nr3c2 -1.047499 0.000004
Prkdc -1.047373 0.000006
Trappc8 -1.045488 0.000001
Zc3h7a -1.044699 0.000006
Arhgef12 -1.114368 -1.043801 0.000001 0.0
Nsd1 -1.043247 0.0
Zfp292 -1.0377 -1.042481 0.000001 0.0
Dnajc1 -1.042034 0.000001
Lrch3 -1.05744 -1.041789 0.0 0.000001
Elp4 -1.158498 -1.040903 0.0 0.000002
Fam120c -1.040768 0.000009
Kmt2c -1.18665 -1.040176 0.0 0.0
Birc6 -1.03906 0.0
Arid1b -1.035444 -1.039026 0.000001 0.0
Rnf216 -1.007176 -1.038781 0.000001 0.0
P4ha1 -1.038611 0.000002
Irf2 -1.27092 -1.03832 0.000001 0.000001
Cyrib -1.236229 -1.037215 0.0 0.000004
Cblb -1.035991 0.000002
Sfswap -1.131248 -1.034404 0.000001 0.000001
Tmem245 -1.032407 0.000002
Eps15l1 -1.031977 0.000001
Trpm7 -1.031837 0.000001
Bcas3 -1.07355 -1.031474 0.0 0.0
Cdk12 -1.031238 0.0
Hmbox1 -1.01985 -1.030377 0.0 0.000002
Gtf2a1 -1.030351 0.000001
Xrcc4 -1.030218 0.000002
Cep350 -1.117219 -1.029798 0.0 0.0
Wdr37 -1.029359 0.000001
Ncoa6 -1.010685 -1.028472 0.000003 0.000007
Hdac4 -1.027947 0.000006
Usp40 -1.187467 -1.027877 0.000001 0.000003
Snap23 -1.0276 0.000009
Lnpep -1.106193 -1.026508 0.000001 0.000003
Spag9 -1.113304 -1.026365 0.0 0.0
Rbm6 -1.025523 0.0
Zcchc7 -1.046159 -1.025514 0.0 0.0
Tasor -1.116647 -1.025185 0.0 0.0
Ank2 -1.12366 -1.023358 0.0 0.000007
Rabep1 -1.145179 -1.023303 0.0 0.000001
Slc38a6 -1.023266 0.000009
Larp4b -1.018161 -1.022693 0.000003 0.0
Gatad2b -1.022442 0.000001
Dennd1a -1.127119 -1.022056 0.0 0.0
Smg6 -1.020681 0.0
Jmjd1c -1.126862 -1.020211 0.0 0.0
Ranbp9 -1.219734 -1.018974 0.0 0.000001
Atp11c -1.137506 -1.01858 0.000008 0.000004
Mycbp2 -1.001901 -1.018552 0.0 0.0
Rbm33 -1.018146 0.0
Dop1a -1.017908 0.000001
Dnm2 -1.288592 -1.017427 0.0 0.000004
Xpr1 -1.064153 -1.015013 0.000009 0.000002
Magi1 -1.254253 -1.014334 0.0 0.0
Ubac2 -1.334667 -1.01341 0.0 0.000003
Rsrc1 -1.022695 -1.011142 0.000001 0.000002
Babam2 -1.010145 0.000002
Atp9b -1.002789 -1.010089 0.000001 0.0
Smyd3 -1.00996 0.0
Eya3 -1.00584 0.000006
Gpatch8 -1.05203 -1.0055 0.000001 0.0
Phip -1.004013 0.0
Fam193a -1.004002 0.0
Memo1 -1.00349 0.000001
Cep83 -1.003376 0.000009
Rsf1 -1.017571 -1.000922 0.0 0.0
Atxn2 -1.000783 0.000005
log_fc adj_p_value
Condition variable
Ground Control Gm6145 -3.573377 4.522636e-06
E130114P18Rik -3.414828 3.313507e-06
Gm12239 -2.883274 3.700958e-06
Slc6a11 -2.790756 1.808033e-06
Arhgef26 -2.616303 1.392169e-06
Slc1a2 -2.577479 4.539396e-08
Pla2g7 -2.519176 1.003768e-06
Diaph3 -2.513137 1.672402e-06
Igfbp6 2.477248 1.230298e-08
Klf15 -2.404960 8.116361e-06
Luzp2 -2.366946 7.099937e-08
Mmd2 -2.365859 8.041800e-07
Rapgef3 -2.362625 5.404788e-07
Gm3764 -2.259300 4.838744e-06
Cecr2 -2.233255 8.116361e-06
Asrgl1 -2.208509 3.607053e-07
Lypd6 -2.192115 3.310536e-07
Ccn3 2.188541 2.805913e-06
Nckap5 -2.168645 1.002755e-06
Gm37298 -2.111420 7.769395e-06
Maml2 -2.081038 5.691145e-07
Gm16091 -2.025520 1.257783e-06
Aldoc -1.956697 4.522636e-06
Npas3 -1.921641 6.456072e-07
Ndrg2 -1.915704 7.229640e-06
Tesk2 -1.880962 3.313388e-07
Acsl3 -1.853781 9.431177e-08
Gfra1 -1.844610 7.454419e-06
Nim1k -1.827430 5.526595e-06
Oma1 -1.823862 3.084900e-06
Sash1 -1.817687 6.596238e-08
Arsk -1.754333 1.094137e-06
Ptch1 -1.737881 1.588596e-07
Gm26618 -1.734147 1.392169e-06
Tprkb -1.731847 1.434050e-06
Dtna -1.707073 3.640761e-08
Stambpl1 -1.706047 2.739354e-06
Sec23ip -1.705224 1.553295e-07
Zfpm2 -1.703982 1.120658e-06
Ugp2 -1.676430 1.081473e-07
Tsc22d4 -1.661644 3.822588e-06
B930095G15Rik -1.656557 6.488290e-06
Dhtkd1 -1.627498 6.759036e-07
Myo6 -1.624461 4.587572e-06
Pik3r1 -1.617184 6.994977e-08
Chn2 -1.596501 4.218427e-06
Pcx -1.587668 3.110172e-06
Tmem243 -1.574139 3.586349e-07
Mtmr10 -1.572795 2.564116e-06
Lifr -1.571746 2.003774e-06
Nfib -1.566654 6.862877e-07
Slc8a1 -1.561550 1.629545e-06
Slc9a9 -1.557398 5.662713e-06
Ivd -1.557192 5.095237e-07
Ccdc62 -1.554425 7.804236e-06
Pcdh11x -1.554361 3.774851e-06
Ccdc93 -1.545851 5.340672e-06
Sema6d -1.534389 5.231307e-07
Trub2 -1.525946 1.340431e-06
Pot1b -1.513722 4.587572e-06
Map2k6 -1.512163 4.713405e-06
Pcdh9 -1.502747 3.590799e-06
Scfd2 -1.480469 3.981344e-08
Nsdhl -1.474669 8.777911e-06
Camk2g -1.456779 5.662713e-06
Gpd2 -1.452743 6.448043e-06
Washc2 -1.451305 9.917344e-08
Septin7 -1.449975 5.370576e-06
Tmtc2 -1.448533 1.059784e-06
Ccdc50 -1.447676 1.430946e-07
Hivep1 -1.443925 9.760159e-07
Abr -1.443667 1.322976e-06
Cdh10 -1.439354 7.904395e-07
Gm4258 -1.439126 7.737143e-06
Zfp236 -1.426366 3.757451e-08
Arhgap35 -1.417262 6.880635e-08
Ppp2r5a -1.408267 2.519228e-06
Tmcc3 -1.406169 8.450970e-06
Wbp1l -1.404148 7.488597e-07
Rhobtb3 -1.402055 7.947613e-07
2610035D17Rik -1.401872 5.703400e-06
Itgav -1.400945 5.465919e-06
Mgll -1.400018 1.434050e-06
Asap2 -1.389618 5.223541e-06
Neo1 -1.380654 6.330896e-08
Creb1 -1.379590 7.357258e-08
Ice2 -1.372636 1.470231e-06
Cwf19l2 -1.371062 2.766886e-08
Hip1 -1.366718 8.450970e-06
Atxn7l1 -1.366285 6.736806e-07
Peli2 -1.364201 5.313785e-06
Rab8b -1.358611 3.813238e-06
Cc2d2a -1.350325 3.278794e-06
Utp25 -1.346449 5.675781e-06
Prkca -1.342159 7.505257e-08
Dlc1 -1.340733 5.587188e-07
Gak -1.340573 4.587572e-06
Itprid2 -1.332288 7.020289e-06
Rnf146 -1.330196 4.960780e-06
C230086J09Rik -1.329386 7.288469e-06
Cadm1 -1.328480 7.454080e-08
Rnf13 -1.322202 6.388942e-06
Unk -1.317669 2.148272e-06
Cdh2 -1.316369 4.067037e-06
Itsn1 -1.315967 1.536148e-06
Pola1 -1.315773 3.726800e-06
Rab10 -1.307995 7.988646e-09
Arhgef11 -1.307824 1.081473e-07
Daam1 -1.302063 5.268467e-06
Apc -1.298690 1.369047e-08
Ptprj -1.296744 3.187535e-06
Ncam1 -1.296248 4.800711e-06
Naaladl2 -1.294446 1.200129e-06
Washc4 -1.294124 3.472633e-07
Mgat5 -1.292674 5.612012e-06
Slk -1.290791 1.808033e-06
Mtss1 -1.288529 3.700958e-06
Mef2a -1.287439 4.902157e-06
Bach2 -1.282681 7.505257e-08
Rasa3 -1.280034 8.300246e-06
Wdr70 -1.277005 2.564096e-07
B3galt1 -1.267652 8.832137e-06
Sema4d -1.263339 8.463073e-06
Fbxl4 -1.262910 2.805913e-06
Desi2 -1.262064 3.065979e-07
Rasa2 -1.259666 2.521782e-06
Fam172a -1.258529 6.462928e-09
Gnao1 -1.257994 7.983537e-06
Ppp6r2 -1.257855 6.862877e-07
Slc25a12 -1.256374 4.220338e-08
Cnot1 -1.255686 2.890293e-07
Ammecr1l -1.255162 9.823754e-06
Hltf -1.245190 5.159067e-06
Kif1b -1.237999 1.127198e-07
B230307C23Rik -1.233874 9.530974e-06
Wdr20 -1.232362 7.121293e-06
Naa35 -1.229264 4.902157e-06
Pikfyve -1.226793 3.131608e-07
Mat2a -1.217237 4.185987e-07
Numb -1.213336 6.565357e-06
Spred1 -1.205513 4.862312e-08
Wsb1 -1.204608 8.727139e-07
Oxr1 -1.204481 2.324424e-06
Wdr44 -1.204101 3.705113e-06
Cdc42se2 -1.203274 1.200129e-06
Kdm4c -1.203249 2.349564e-07
Gphn -1.200420 2.479579e-07
Chpt1 -1.200016 2.076011e-06
Crtc3 -1.196631 2.635534e-06
Ankib1 -1.195997 4.397423e-07
Tmcc1 -1.192550 2.349564e-07
Phactr3 -1.185863 9.340727e-06
Slain2 -1.184776 6.448043e-06
Atp2c1 -1.183071 8.613809e-08
Pan3 -1.182425 2.663665e-08
Extl3 -1.180863 6.818509e-06
Smarca2 -1.180437 7.454080e-08
Btbd3 -1.180249 9.261294e-06
Prpf39 -1.178642 1.823962e-07
Tanc2 -1.176917 8.176494e-06
Csnk2a1 -1.175128 1.340431e-06
Nlk -1.173702 2.324424e-06
Galnt2 -1.173301 5.223541e-06
Wapl -1.171429 1.459814e-07
Cnot2 -1.170786 2.871885e-08
Marchf2 -1.168860 8.037853e-06
Rc3h1 -1.166094 8.758108e-08
Scaper -1.164729 1.205154e-06
Huwe1 -1.164458 1.127198e-07
Eif4g3 -1.159608 7.988646e-09
9330159F19Rik -1.158959 7.004815e-06
Pigu -1.154961 4.329854e-06
Zfyve16 -1.153574 6.761511e-06
Mcu -1.146269 6.421886e-06
Syncrip -1.145057 4.500749e-06
Tcf4 -1.143223 1.392169e-06
Fryl -1.143214 6.410202e-07
Senp1 -1.140985 9.340727e-06
Cpeb2 -1.137233 2.076011e-06
Zfp638 -1.135153 4.428509e-08
Psd3 -1.133795 2.932375e-07
Actr2 -1.133172 4.587572e-06
Kat6a -1.130519 8.970757e-07
Zfp106 -1.128510 7.347406e-06
Hnrnpa2b1 -1.127334 3.065979e-07
Zfand3 -1.126955 1.588596e-07
Arid1a -1.124097 7.864408e-07
Akap7 -1.123973 5.268467e-06
Jarid2 -1.123364 3.607190e-08
Peli1 -1.121964 1.801566e-06
Ppp4r1 -1.121127 5.379300e-06
N4bp2l2 -1.119830 3.681293e-07
Git2 -1.119562 7.965338e-06
Vps50 -1.118761 4.218427e-06
Fip1l1 -1.118132 3.525986e-07
Galnt1 -1.117342 7.638523e-07
Top2b -1.116615 1.454133e-06
Gtdc1 -1.114814 2.329193e-07
Nf1 -1.114517 1.492420e-09
Morc2a -1.111535 9.530974e-06
AW554918 -1.110554 8.470382e-08
Asxl2 -1.109968 1.337326e-07
Map4 -1.108524 1.300559e-06
Rif1 -1.106647 4.344484e-06
Cask -1.103665 3.387730e-06
Ckap5 -1.103639 5.231307e-07
Depdc5 -1.103416 9.114696e-06
Uimc1 -1.102979 5.465919e-06
Vps13d -1.102420 3.150288e-07
Zmym4 -1.101603 4.380189e-08
Tlk2 -1.098283 3.313388e-07
Sgce -1.096091 1.635591e-06
Rasa1 -1.095814 2.560153e-07
Nrf1 -1.095478 4.397423e-07
Nr3c1 -1.095468 6.880635e-08
Ncor1 -1.093589 1.043336e-08
Ehmt1 -1.093530 1.983920e-06
Ubn1 -1.091978 5.268467e-06
Kdm6a -1.088983 9.159620e-08
Kpna3 -1.088624 4.587572e-06
Tsc22d2 -1.085540 8.118987e-07
Btbd7 -1.084467 4.209651e-07
Ubr3 -1.083015 1.316637e-08
Cux1 -1.082372 8.925063e-06
Phf14 -1.082359 6.507224e-07
Cbl -1.081227 6.343860e-06
Rab12 -1.079538 7.911894e-06
Malat1 -1.079456 2.678250e-06
Asap1 -1.078946 8.345314e-07
E130307A14Rik -1.076968 3.785909e-06
Sik3 -1.075276 5.282842e-06
Brwd3 -1.071343 4.925441e-06
Megf9 -1.069265 6.755278e-06
Lrba -1.068716 1.628891e-06
Rnf130 -1.068456 1.138995e-06
4732471J01Rik -1.068404 4.630704e-06
Mysm1 -1.066166 4.688774e-06
Cop1 -1.065041 5.806975e-07
Smurf2 -1.061977 1.445069e-07
Arfgef1 -1.061893 4.352756e-07
Ubr1 -1.061293 8.391277e-07
St7l -1.058720 2.452596e-06
Ppm1b -1.058635 1.220624e-06
Gapvd1 -1.058337 7.593985e-07
Herc1 -1.058221 8.815958e-07
Dmtf1 -1.058108 6.753267e-06
Arid2 -1.055463 3.472633e-07
Rbm25 -1.052244 2.755831e-09
Nr1d2 -1.051903 4.960780e-06
Ric1 -1.051242 5.582738e-06
Sdccag8 -1.050709 1.918954e-06
Abcc5 -1.050198 4.932853e-07
Psme4 -1.048667 3.954945e-07
Pias2 -1.048530 3.672693e-06
Dennd5a -1.048221 1.966510e-06
Rbm26 -1.048027 1.316286e-07
Wdfy3 -1.046912 2.167377e-08
Spast -1.046348 4.751415e-06
Ext1 -1.045646 5.178671e-06
Dyrk1a -1.044700 3.345228e-07
Pcm1 -1.043335 4.491349e-07
Gigyf2 -1.042536 2.441935e-07
Gm49797 -1.041415 9.068360e-06
Fbxo38 -1.040840 1.392122e-06
Rere -1.038889 4.429356e-08
Nfat5 -1.038691 5.631573e-07
Marchf7 -1.038086 1.434050e-06
Herc2 -1.036836 4.932853e-07
Arid4b -1.036753 6.862877e-07
Wac -1.035655 1.381108e-06
Ncoa2 -1.035562 8.863695e-07
Specc1l -1.034861 1.263371e-06
Atrx -1.033631 8.949020e-08
Ppp6r3 -1.031682 3.091924e-06
Ago3 -1.029933 3.310284e-07
Epc2 -1.028556 8.832137e-06
Stk3 -1.028305 7.244439e-06
Trip11 -1.027546 2.893612e-06
U2surp -1.027529 1.015861e-06
Uvrag -1.025144 3.700958e-06
Slc25a36 -1.024973 4.925441e-06
Ccnt2 -1.024334 4.751415e-06
Faf1 -1.023775 1.297364e-06
Tmem165 -1.022415 9.268712e-06
Rhot1 -1.021899 7.804236e-06
Dcaf6 -1.021678 7.244439e-06
Xrn2 -1.021641 5.477727e-06
Ints6 -1.020837 3.374238e-06
Insr -1.019854 7.904395e-07
Nipbl -1.019622 7.988646e-09
Slc38a9 -1.019436 1.340431e-06
Thrap3 -1.019224 2.242012e-06
Ppp1r12a -1.018894 9.431177e-08
Tjp1 -1.018081 1.055035e-06
Pten -1.015087 6.152049e-07
Relch -1.014096 5.343400e-06
Npepps -1.014025 2.324424e-06
Map2k5 -1.014022 1.115852e-06
Chd6 -1.011588 5.134139e-08
Pafah1b1 -1.009340 2.349564e-07
Camsap2 -1.008730 6.268956e-06
Tra2a -1.007333 9.431177e-08
Rbm39 -1.005894 8.222625e-09
Son -1.005566 1.588596e-07
Smg7 -1.004829 7.801740e-07
Zkscan3 -1.003514 3.187535e-06
Pum1 -1.003214 3.532630e-06
Pabpn1 -1.002490 7.801740e-07
Setd5 -1.001812 7.454080e-08
Fto -1.001111 4.587572e-06
Rbm5 -1.000905 1.258620e-07
Aff4 -1.000789 5.675781e-06
Numa1 -1.000670 3.625730e-06
Usp34 -1.000268 7.818954e-07
Sesn1 -1.000007 2.651364e-06
Out[192]:
| log_fc | adj_p_value | ||
|---|---|---|---|
| Condition | variable | ||
| Space Flight | Phkg1 | -2.968877 | 5.854433e-06 |
| Acsbg1 | -2.952229 | 3.453443e-06 | |
| Lrig1 | -2.744472 | 4.315019e-07 | |
| Adhfe1 | -2.726487 | 7.208347e-06 | |
| Ptgs2 | 2.672912 | 2.804616e-06 | |
| Irak2 | -2.447392 | 2.719703e-06 | |
| Sema4b | -2.344446 | 1.122562e-06 | |
| Myo10 | -2.299259 | 5.832356e-07 | |
| Fosb | 2.253945 | 5.213249e-07 | |
| Gpld1 | -2.172901 | 1.756315e-06 | |
| Rcbtb2 | -2.024693 | 6.486410e-06 | |
| Trp53bp2 | -2.018176 | 3.016868e-06 | |
| Sat1 | -1.972960 | 1.991226e-06 | |
| Cables1 | -1.954440 | 1.181741e-06 | |
| Pag1 | -1.917645 | 6.822238e-06 | |
| Egr4 | 1.907752 | 1.420553e-06 | |
| Soat1 | -1.870687 | 9.563059e-08 | |
| Junos | -1.869604 | 8.063751e-06 | |
| Limk1 | -1.867847 | 9.040912e-07 | |
| Psd2 | -1.845588 | 9.937513e-06 | |
| Gpam | -1.826651 | 3.533198e-06 | |
| Crlf3 | -1.755764 | 1.766446e-06 | |
| Rbm12 | -1.699022 | 2.290756e-06 | |
| Rft1 | -1.694305 | 5.572557e-06 | |
| Rnls | -1.685472 | 4.466070e-06 | |
| Eps15 | -1.664543 | 3.229966e-07 | |
| Aff1 | -1.657693 | 7.870402e-06 | |
| Nek7 | -1.619015 | 7.780528e-07 | |
| Golim4 | -1.594119 | 6.532917e-06 | |
| Denn2b | -1.584930 | 9.141516e-06 | |
| Cep192 | -1.530330 | 1.569122e-07 | |
| Atp7a | -1.490834 | 2.780767e-06 | |
| Ppcdc | -1.490009 | 4.066629e-06 | |
| Pepd | -1.481809 | 2.403473e-06 | |
| Arhgef1 | -1.479845 | 5.720882e-07 | |
| Dram2 | -1.469943 | 1.327139e-06 | |
| Hdac1 | -1.465360 | 1.146259e-06 | |
| Afg1l | -1.462639 | 9.877643e-08 | |
| Zfp652 | -1.457750 | 1.856272e-07 | |
| Aven | -1.452047 | 3.623400e-06 | |
| Elovl5 | -1.451515 | 7.309966e-06 | |
| Foxn2 | -1.448836 | 8.204402e-06 | |
| 4931406C07Rik | -1.448177 | 4.694189e-06 | |
| Wdpcp | -1.443782 | 3.794296e-08 | |
| Mlxip | -1.416183 | 3.393277e-06 | |
| 2610005L07Rik | -1.416047 | 1.024364e-06 | |
| Ift140 | -1.415683 | 2.627021e-06 | |
| Pcyt1b | -1.414975 | 8.170542e-06 | |
| Fkbp15 | -1.408662 | 9.665160e-08 | |
| E2f3 | -1.395828 | 4.763160e-06 | |
| Bbs9 | -1.377205 | 2.101987e-07 | |
| Kdm3b | -1.372672 | 5.711425e-09 | |
| Anks1 | -1.371977 | 2.261408e-07 | |
| Ptprm | -1.367174 | 9.733288e-07 | |
| Fmn1 | -1.357201 | 5.213769e-07 | |
| Slc30a7 | -1.356308 | 3.438518e-07 | |
| 2700049A03Rik | -1.354757 | 5.754686e-07 | |
| Hibch | -1.352369 | 2.563184e-06 | |
| Abcb7 | -1.349580 | 3.010942e-07 | |
| Rap1a | -1.339492 | 2.886735e-06 | |
| Zbtb20 | -1.333404 | 4.539859e-06 | |
| Tubgcp5 | -1.331810 | 2.179234e-06 | |
| Ranbp10 | -1.331549 | 4.464255e-07 | |
| Xpnpep1 | -1.330197 | 6.846016e-07 | |
| Mettl15 | -1.329575 | 4.539668e-06 | |
| Metap1d | -1.328115 | 2.054064e-06 | |
| Arhgap12 | -1.318575 | 5.014013e-07 | |
| Kiz | -1.313942 | 3.708510e-06 | |
| Marf1 | -1.301696 | 5.420371e-07 | |
| Exoc2 | -1.299300 | 1.233345e-07 | |
| Ikbkb | -1.296711 | 4.609626e-06 | |
| Iffo1 | -1.292995 | 2.563184e-06 | |
| Ets2 | 1.288177 | 7.924873e-06 | |
| 1110019D14Rik | -1.287858 | 9.937513e-06 | |
| Zfp609 | -1.277499 | 1.567009e-09 | |
| Rbmx | -1.275619 | 8.547310e-06 | |
| Rsu1 | -1.274831 | 5.041942e-07 | |
| Ngly1 | -1.266358 | 8.541865e-06 | |
| Zhx3 | -1.257531 | 2.961799e-06 | |
| Pvt1 | -1.256151 | 9.284601e-06 | |
| Clint1 | -1.254483 | 2.261408e-07 | |
| Ksr1 | -1.253239 | 9.040912e-07 | |
| Ints6l | -1.248422 | 2.563184e-06 | |
| Wrn | -1.248163 | 2.749622e-06 | |
| Aatf | -1.246979 | 5.577270e-07 | |
| Peak1 | -1.242027 | 3.000661e-06 | |
| Asph | -1.241851 | 7.861102e-06 | |
| 5430405H02Rik | -1.240385 | 9.284601e-06 | |
| Garre1 | -1.240103 | 2.074677e-06 | |
| Agps | -1.237916 | 4.975544e-08 | |
| Ide | -1.235851 | 3.518704e-07 | |
| 5830417I10Rik | -1.230097 | 6.251043e-06 | |
| Caap1 | -1.229411 | 2.302285e-07 | |
| Cdin1 | -1.222015 | 3.565152e-06 | |
| Srbd1 | -1.221812 | 8.204402e-06 | |
| Nav2 | -1.218302 | 1.324619e-06 | |
| Gpatch2l | -1.214858 | 1.735987e-06 | |
| Adam10 | -1.205012 | 6.268891e-08 | |
| Gpatch2 | -1.200008 | 3.981031e-07 | |
| Abl1 | -1.192791 | 9.789789e-06 | |
| Zfp326 | -1.191233 | 1.592409e-06 | |
| Ufl1 | -1.188672 | 4.485614e-07 | |
| Arhgap17 | -1.187129 | 8.868777e-06 | |
| Srsf4 | -1.186111 | 1.675275e-06 | |
| Zfand6 | -1.185857 | 2.515842e-06 | |
| Dennd4c | -1.181313 | 9.144167e-06 | |
| Nfyc | -1.174726 | 1.484249e-06 | |
| 5031425E22Rik | -1.173343 | 9.142845e-08 | |
| Kdm3a | -1.169541 | 5.213769e-07 | |
| Fbxl12 | -1.169216 | 9.575757e-06 | |
| Urgcp | -1.168905 | 7.902936e-07 | |
| Raf1 | -1.167525 | 7.378319e-07 | |
| Armh3 | -1.166860 | 1.422403e-06 | |
| Zfyve1 | -1.165307 | 2.627021e-06 | |
| Ccdc58 | -1.164089 | 4.388845e-06 | |
| Mapk14 | -1.155314 | 1.420183e-06 | |
| Lrrfip2 | -1.152523 | 1.177683e-06 | |
| Gpbp1l1 | -1.150877 | 1.347075e-06 | |
| Cdc27 | -1.150376 | 9.835313e-07 | |
| Arid4a | -1.150133 | 5.041942e-07 | |
| Ptpn9 | -1.148738 | 1.120308e-06 | |
| Ralgapb | -1.148341 | 7.119410e-07 | |
| Trim24 | -1.146034 | 1.425088e-06 | |
| Rock1 | -1.139758 | 4.958736e-10 | |
| Tbcel | -1.137367 | 6.591810e-06 | |
| Zfp407 | -1.136468 | 9.173403e-07 | |
| Cog5 | -1.135283 | 1.471647e-07 | |
| Chaserr | -1.132490 | 6.784289e-08 | |
| Pwwp2a | -1.130970 | 6.022605e-07 | |
| Pkd2 | -1.130946 | 2.804616e-06 | |
| Tbc1d9b | -1.125478 | 6.923577e-06 | |
| Wwp1 | -1.125445 | 2.402106e-06 | |
| Diaph2 | -1.124863 | 2.399381e-07 | |
| Prdm2 | -1.121082 | 1.039890e-06 | |
| Immp2l | -1.119868 | 3.287548e-07 | |
| Exoc4 | -1.119411 | 4.958736e-10 | |
| Nt5c2 | -1.118587 | 1.296364e-06 | |
| Setd2 | -1.117000 | 2.230443e-07 | |
| Fus | -1.116371 | 2.099518e-08 | |
| Mia2 | -1.116298 | 1.296364e-06 | |
| Fcho2 | -1.115583 | 6.869002e-07 | |
| Orc3 | -1.112079 | 2.056612e-06 | |
| Rsrp1 | -1.111908 | 3.303286e-07 | |
| Ralgps1 | -1.111629 | 4.095232e-06 | |
| Sh3kbp1 | -1.110383 | 9.982637e-06 | |
| Gbf1 | -1.107221 | 1.188586e-08 | |
| Sh3glb1 | -1.106899 | 1.224946e-06 | |
| Pbrm1 | -1.106482 | 8.870380e-08 | |
| Sptlc2 | -1.106367 | 1.566858e-06 | |
| Ptar1 | -1.105347 | 5.704473e-06 | |
| Strada | -1.102824 | 4.279465e-06 | |
| Lin52 | -1.102008 | 3.709172e-06 | |
| Dido1 | -1.093208 | 4.095232e-06 | |
| Mon2 | -1.092680 | 1.819408e-06 | |
| AU040320 | -1.092050 | 2.841975e-06 | |
| Cyb5r4 | -1.091985 | 3.791495e-06 | |
| Hectd1 | -1.090760 | 5.728092e-08 | |
| Cnnm2 | -1.088328 | 9.937513e-06 | |
| Spata5 | -1.087306 | 3.478038e-06 | |
| Dcaf8 | -1.086865 | 1.558715e-07 | |
| Supt3 | -1.086731 | 1.819408e-06 | |
| Med13l | -1.086676 | 3.794296e-08 | |
| Esco1 | -1.086060 | 3.416309e-07 | |
| Stx8 | -1.084876 | 5.787064e-07 | |
| Dpy19l4 | -1.077825 | 3.228678e-06 | |
| Aftph | -1.076983 | 1.420183e-06 | |
| Dcp1b | -1.073342 | 7.589923e-06 | |
| Cnot4 | -1.071681 | 1.036173e-08 | |
| Rfx3 | -1.071429 | 6.654514e-06 | |
| Zup1 | -1.071179 | 3.296595e-06 | |
| Ppip5k2 | -1.070868 | 6.607341e-06 | |
| Fbxl20 | -1.070577 | 3.728932e-08 | |
| Zfp277 | -1.069330 | 8.547457e-06 | |
| Eri3 | -1.069108 | 8.204402e-06 | |
| Gm21992 | -1.068347 | 4.466946e-06 | |
| Usp8 | -1.066508 | 7.869947e-06 | |
| Reps1 | -1.061845 | 4.554991e-06 | |
| Vps54 | -1.060900 | 1.994828e-07 | |
| Fnbp4 | -1.060448 | 5.123507e-07 | |
| Zfc3h1 | -1.060356 | 4.853515e-08 | |
| Phc3 | -1.060246 | 3.585068e-07 | |
| Pik3ca | -1.057906 | 7.946576e-07 | |
| Cd2ap | -1.054839 | 4.464255e-07 | |
| Tasor2 | -1.051641 | 1.766446e-06 | |
| Copg2 | -1.051164 | 2.780378e-06 | |
| Tut7 | -1.050976 | 9.583436e-07 | |
| Stk38 | -1.050363 | 2.680057e-06 | |
| Nr3c2 | -1.047499 | 3.511396e-06 | |
| Prkdc | -1.047373 | 6.327339e-06 | |
| Trappc8 | -1.045488 | 1.181741e-06 | |
| Zc3h7a | -1.044699 | 5.639096e-06 | |
| Nsd1 | -1.043247 | 1.302321e-09 | |
| Dnajc1 | -1.042034 | 5.303390e-07 | |
| Fam120c | -1.040768 | 9.441782e-06 | |
| Birc6 | -1.039060 | 4.958736e-10 | |
| P4ha1 | -1.038611 | 2.297349e-06 | |
| Cblb | -1.035991 | 2.484853e-06 | |
| Tmem245 | -1.032407 | 2.057869e-06 | |
| Eps15l1 | -1.031977 | 1.414918e-06 | |
| Trpm7 | -1.031837 | 1.089956e-06 | |
| Cdk12 | -1.031238 | 7.209359e-08 | |
| Gtf2a1 | -1.030351 | 1.242729e-06 | |
| Xrcc4 | -1.030218 | 2.037547e-06 | |
| Wdr37 | -1.029359 | 1.484249e-06 | |
| Hdac4 | -1.027947 | 6.363889e-06 | |
| Snap23 | -1.027600 | 8.660642e-06 | |
| Rbm6 | -1.025523 | 7.694457e-09 | |
| Slc38a6 | -1.023266 | 8.872769e-06 | |
| Gatad2b | -1.022442 | 5.213249e-07 | |
| Smg6 | -1.020681 | 4.109565e-08 | |
| Rbm33 | -1.018146 | 8.506466e-08 | |
| Dop1a | -1.017908 | 8.620697e-07 | |
| Babam2 | -1.010145 | 1.534145e-06 | |
| Smyd3 | -1.009960 | 2.603839e-07 | |
| Eya3 | -1.005840 | 5.785732e-06 | |
| Phip | -1.004013 | 9.695621e-08 | |
| Fam193a | -1.004002 | 6.766003e-08 | |
| Memo1 | -1.003490 | 1.280211e-06 | |
| Cep83 | -1.003376 | 8.660642e-06 | |
| Atxn2 | -1.000783 | 4.694189e-06 |
Find Top Senescence Genes by Cell Type¶
Among cells in top x% of senescence scores
In [ ]:
# Overall
# mean_expr = np.asarray(self.rna[self.rna.obs["Senescence_CellType"].isin(
# high_sen_cts)][:, genes].X.mean(axis=0)).ravel()
# df_expr = pd.DataFrame({"gene": genes, "mean_expression": mean_expr})
# df_expr = df_expr.sort_values("mean_expression", ascending=False)
# top_genes = df_expr["gene"].iloc[:10]
# df_expr.T
# Top 10 for Each Cell Type
genes_valid = [g for g in genes if g in self.rna.var_names]
rna_subset = self.rna[self.rna.obs["Senescent_Cell"].isin([1, True])]
expr_subset = rna_subset[:, genes_valid].X
expr_matrix = expr_subset.toarray() if issparse(
expr_subset) else expr_subset # robust sparse-to-dense
df_expr = pd.DataFrame(expr_matrix, columns=genes_valid)
df_expr.loc[:, col_celltype] = rna_subset.obs[col_celltype].values
top_genes = df_expr.groupby(col_celltype).mean(numeric_only=True).apply(
lambda x: x.sort_values(ascending=False).head(10).index.tolist(), axis=1)
top_genes_dict = dict(top_genes)
top_genes = list(np.unique(top_genes.explode()))
print(top_genes_dict)
# print("\n\n", self.rna.obs[col_celltype].unique())
# np.array([x in self.rna.obs[col_celltype].values for x in top_genes_dict])
Query Literature, Atlases, & Pathways¶
In [ ]:
results_toppgene = scflow.pp.annotate_by_toppgene(
top_genes_dict, species=species, min_genes=2,
categories=["Pathway", "PubMed"])
restg = results_toppgene.Name.sort_values().reset_index(
"Category", drop=True).reset_index("ID").groupby(
"Gene Set").apply(lambda x: x.drop("ID", axis=1).assign(
ID=np.arange(len(x))), include_groups=False).set_index(
"ID", append=True).reset_index(0, drop=True).unstack("ID")
all_labels_litpath = restg.stack().Name.unique()
restg = restg.apply(lambda x: pd.Series(dict(zip(all_labels_litpath, ["x" if (
i in list(x.Name)) else "" for i in all_labels_litpath]))), axis=1).T
print(results_toppgene[["Name", "Symbols"]])
restg
Plot¶
In [ ]:
# Top 10 Senescence Genes or All?
ggg = top_genes
# ggg = genes
# Mask for High Senescence Subset & Helper Variable for Figure Dimensions
subset = self.rna.obs["Senescent_Cell"].isin([1, True])
num_cts = len(self.rna[subset].obs[col_celltype].unique())
# Plot SnC Markers Overlap with Marker Sets
sc.pl.violin(self.rna[subset], set(genes).intersection(
marker_gene_sets.explode().unique()),
groupby=col_batch, rotation=90) # plot overlap w/ SnC markers
# Plot Most Highly-Expressed Genes in Each Senescent Cell Type
_ = self.plot(kind=["matrix", "heat"], genes=top_genes,
col_celltype=col_celltype, layer="log1p",
subset=subset, dendrogram=True, swap_axes=False, heat=dict(
dendrogram=False), standard_scale="var",
figsize=(len(top_genes) / 1.5, num_cts * 1.5))
# Grouped by Top Cell Types
_ = self.plot(kind=["matrix", "heat"], genes=top_genes_dict,
standard_scale="obs", col_celltype=col_celltype,
layer="log1p", swap_axes=False, figsize=(len(ggg), num_cts),
subset=subset, dendrogram=True)
Write Object¶
In [ ]:
# Send Email with Output When Done
if email is not None and html_out is not None:
os.system(f"jupyter nbconvert --to html {cur_file}")
os.system(f"echo 'yay' | mutt -s 'JOB DONE' -a {html_out} -- {email}")
if overwrite is True or os.path.exists() is False:
self.rna.write_h5ad(file_path_new)
else:
print(f"\n\n\n{'=' * 120}\nOVERWRITE IS FALSE..."
f"NOT WRITING OBJECT!!!\n{'=' * 120}\n\n\n")
[NbConvertApp] Converting notebook /home/easlinger/AWG/ADBR/senescence/analyze_senescence_celltype_specific_threshold.ipynb to html [NbConvertApp] WARNING | Alternative text is missing on 102 image(s). [NbConvertApp] Writing 16535612 bytes to /home/easlinger/AWG/ADBR/senescence/analyze_senescence_celltype_specific_threshold.html Authenticating (PLAIN)... Closing connection to imap.gmail.com...
SCRATCH¶
In [235]:
# df_degs = out_edgr_old_sf_v_gc.copy()
df_degs = out_edgr_batches_snc_top.copy()
# df_degs = res_snc_grps.copy()
# factor_name = col_celltype
factor_name = col_condition
names = gp.get_library_name()
# [i for i in names if "brain" in i.lower() or "mouse" in i.lower()]
pathways_snc_vs_no, res_pathways_snc_vs_no = {}, {}
thresh_p_path = 0.05
# perts_geo = ["Aging", "Drug", "Disease"]
perts_geo = ["Aging", "Disease"]
other_sets = ["GTEx_Aging_Signatures_2021", f"HDSigDB_{species}_2021",
# "DGIdb_Drug_Targets_2024",
"MSigDB_Hallmark_2020",
"MSigDB_Oncogenic_Signatures", "OMIM_Disease",
# "Reactome_Pathways_2024", "Reactome_2022",
f"WikiPathways_2024_{species}", f"WikiPathways_2019_{species}"]
other_sets += [i for i in names if "GO_Biological_Process" in i]
# if species.lower() == "mouse":
# other_sets += ["PerturbAtlas_MouseGenePerturbationSigs"]
for x in df_degs.reset_index()[factor_name].unique():
pathways_snc_vs_no[x], res_pathways_snc_vs_no[x] = {}, {}
for i in ["up", "down"]:
degs_tmp = df_degs.loc[x]
if i == "up":
degs_tmp = list(degs_tmp[degs_tmp.log_fc > 0].index.values)
else:
degs_tmp = list(degs_tmp[degs_tmp.log_fc < 0].index.values)
if len(degs_tmp) > 0:
g_s = [f"{u}_Perturbations_from_GEO_{i}" for u in perts_geo] + [
f"Disease_Signatures_from_GEO_{i}_2014"] + other_sets
g_s = [g for g in g_s if g in names]
pathways_snc_vs_no[x][i] = gp.enrichr(
gene_list=degs_tmp, gene_sets=g_s,
organism=species, cutoff=0.5)
res_pathways_snc_vs_no[x][i] = pathways_snc_vs_no[x][i].results[
pathways_snc_vs_no[x][i].results[
"Adjusted P-value"] < thresh_p_path]
res_pathways_snc_vs_no[x] = pd.concat(res_pathways_snc_vs_no[x],
names=["Direction"])
res_pathways_snc_vs_no = pd.concat(res_pathways_snc_vs_no,
names=[col_celltype])
res_pathways_snc_vs_no.loc[
:, "Term Short"] = res_pathways_snc_vs_no.Term.apply(
lambda x: re.sub("Expression Of ", "", re.sub(".*.xls", "", re.sub(
".*.XLSX", "",
re.sub(".*Supplementary Table [0-9]+[-]?", "", re.sub(
".*.xlsx.", "", re.sub(".*Supplementary Data ..", "", re.sub(
"PMID([0-9])+", "", re.sub(
"([0-9]+) years v ([0-9]+) years", "\\1 v \\2 yrs.",
re.sub("aging:([0-9]+)", "aging", re.sub(
"([0-9]+) months v ([0-9]+) mo(nths)?",
"\\1 v \\2 mo.", re.sub("SRP[0-9]+ ", "", re.sub(
"GSE[0-9]+ ", "", x)))))))))))))
cts_i = res_pathways_snc_vs_no.reset_index()[col_celltype].unique()
res_pathways_snc_top_vs_no = res_pathways_snc_vs_no.reset_index().groupby([
col_celltype, "Direction"]).apply(lambda x: x.sort_values(
"Adjusted P-value", ascending=True).head(10), include_groups=False)
fig, axes = plt.subplots(*scflow.pl.square_grid(cts_i), figsize=(
20, 25), gridspec_kw=dict(wspace=1.5, top=0.95), squeeze=False)
for i, x in enumerate(cts_i):
gp.dotplot(
res_pathways_snc_top_vs_no.loc[x].reset_index(),
column="Adjusted P-value",
# column="Overlap",
x="Direction", y="Term Short",
ax=axes.flatten()[i], size=20, top_term=100,
title=x, xticklabels_rot=45, yticklabels_rot=45,
show_ring=True, marker="o", cutoff=0.5)
axes.flatten()[i].tick_params(axis="y", labelsize=8,
labelfontfamily="serif")
axes.flatten()[i].tick_params(axis="x", labelsize=10,
labelfontfamily="serif")
axes.flatten()[i].title.set_fontsize(10)
if len(axes.flatten()) > len(cts_i):
for a in axes.flatten()[len(cts_i):]:
a.set_visible(False)
fig.suptitle("Senescent Cell Spaceflight DEGs", fontsize="xx-large",
fontproperties=dict(family="serif"))
res_pathways_snc_top_vs_no[["Term Short", "Genes"] + list(
res_pathways_snc_top_vs_no.columns.difference([
"Term", "Genes", "Term Short"]))]
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms))
Out[235]:
| Term Short | Genes | Adjusted P-value | Combined Score | Gene_set | Odds Ratio | Old Adjusted P-value | Old P-value | Overlap | P-value | level_2 | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| annotation_by_overlap | Direction | ||||||||||||
| Space Flight | 12 Weeks | up | 420 | Electron Transport Chain WP295 | NDUFA8;UQCRB;NDUFA12;NDUFA4;ATP5E;COX4I1;NDUFC1;COX7A2;COX5A;SLC25A4;COX6B1 | 3.777844e-20 | 12289.664347 | WikiPathways_2019_Mouse | 264.212560 | 0 | 0 | 11/103 | 6.296407e-21 | 1494 |
| 481 | Cellular Respiration (GO:0045333) | NDUFA8;NDUFA13;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFC1;COX7A2;COX7C;COX5A;COX6B1 | 9.227446e-20 | 16025.343807 | GO_Biological_Process_2025 | 328.777778 | 0 | 0 | 11/85 | 6.784887e-22 | 1791 | ||
| 440 | Cellular Respiration (GO:0045333) | NDUFA8;NDUFA13;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFC1;COX7A2;COX7C;COX5A;COX6B1 | 9.838085e-20 | 16025.343807 | GO_Biological_Process_2023 | 328.777778 | 0 | 0 | 11/85 | 6.784887e-22 | 1646 | ||
| 482 | Mitochondrial ATP Synthesis Coupled Electron Transport (GO:0042775) | NDUFA8;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFC1;COX7A2;COX7C;COX5A;COX6B1 | 7.782512e-19 | 16634.384561 | GO_Biological_Process_2025 | 362.272727 | 0 | 0 | 10/65 | 1.144487e-20 | 1792 | ||
| 317 | Striatum RNA M22 Module (Darkgreen) | H2AZ1;NDUFA8;NDUFA13;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFC1;COX7A2;COX7C;COX5A;COX6B1;CHCHD2;ATP5E;S... | 1.786554e-18 | 6179.552039 | HDSigDB_Mouse_2021 | 129.869347 | 0 | 0 | 16/613 | 2.162899e-21 | 649 | ||
| 441 | Mitochondrial ATP Synthesis Coupled Electron Transport (GO:0042775) | NDUFA8;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFC1;COX7A2;COX7C;COX5A;COX6B1 | 1.834655e-18 | 14980.922968 | GO_Biological_Process_2023 | 332.000000 | 0 | 0 | 10/70 | 2.530558e-20 | 1647 | ||
| 423 | mitochondrial ATP synthesis coupled electron transport (GO:0042775) | NDUFA8;NDUFA13;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFC1;COX7C;COX5A;COX6B1 | 2.149178e-18 | 14685.170247 | GO_Biological_Process_2021 | 326.540984 | 0 | 0 | 10/71 | 2.944080e-20 | 1501 | ||
| 422 | aerobic electron transport chain (GO:0019646) | NDUFA8;NDUFA13;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFC1;COX7C;COX5A;COX6B1 | 2.149178e-18 | 14980.922968 | GO_Biological_Process_2021 | 332.000000 | 0 | 0 | 10/70 | 2.530558e-20 | 1500 | ||
| 413 | Oxidative Phosphorylation | NDUFA8;UQCRB;NDUFA4;COX4I1;NDUFC1;COX7A2;COX7C;COX5A;SLC25A4;COX6B1;ATP6V1F | 9.409673e-17 | 4989.032439 | MSigDB_Hallmark_2020 | 127.984127 | 0 | 0 | 11/200 | 1.176209e-17 | 1475 | ||
| 483 | Aerobic Electron Transport Chain (GO:0019646) | NDUFA8;UQCRB;NDUFA4;COX4I1;NDUFC1;COX7A2;COX7C;COX5A;COX6B1 | 1.447739e-16 | 11940.792637 | GO_Biological_Process_2025 | 296.404959 | 0 | 0 | 9/64 | 3.193542e-18 | 1793 | ||
| Space Flight | 29 Weeks | up | 1036 | Mitochondrial ATP Synthesis Coupled Electron Transport (GO:0042775) | NDUFA8;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFA1;UQCR11;NDUFC1;COX7A2;COX6C;COX7C | 1.980073e-21 | 23409.272197 | GO_Biological_Process_2025 | 451.000000 | 0 | 0 | 11/65 | 2.869671e-23 | 1618 |
| 1010 | Mitochondrial ATP Synthesis Coupled Electron Transport (GO:0042775) | NDUFA8;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFA1;UQCR11;NDUFC1;COX7A2;COX6C;COX7C | 4.500231e-21 | 21056.609585 | GO_Biological_Process_2023 | 412.676083 | 0 | 0 | 11/70 | 6.923432e-23 | 1553 | ||
| 994 | Electron Transport Chain WP295 | NDUFA8;UQCRB;NDUFA12;NDUFA4;ATP5E;COX4I1;NDUFA1;UQCR11;NDUFC1;COX7A2;COX6C | 1.888922e-20 | 12289.664347 | WikiPathways_2019_Mouse | 264.212560 | 0 | 0 | 11/103 | 6.296407e-21 | 1399 | ||
| 1011 | Cellular Respiration (GO:0045333) | NDUFA8;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFA1;UQCR11;NDUFC1;COX7A2;COX6C;COX7C | 2.205088e-20 | 16025.343807 | GO_Biological_Process_2023 | 328.777778 | 0 | 0 | 11/85 | 6.784887e-22 | 1554 | ||
| 1037 | Cellular Respiration (GO:0045333) | NDUFA8;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFA1;UQCR11;NDUFC1;COX7A2;COX6C;COX7C | 2.340786e-20 | 16025.343807 | GO_Biological_Process_2025 | 328.777778 | 0 | 0 | 11/85 | 6.784887e-22 | 1619 | ||
| 1038 | Aerobic Electron Transport Chain (GO:0019646) | NDUFA8;UQCRB;NDUFA4;COX4I1;NDUFA1;UQCR11;NDUFC1;COX7A2;COX6C;COX7C | 2.228347e-19 | 17004.756449 | GO_Biological_Process_2025 | 369.000000 | 0 | 0 | 10/64 | 9.688466e-21 | 1620 | ||
| 1012 | Aerobic Electron Transport Chain (GO:0019646) | NDUFA8;UQCRB;NDUFA4;COX4I1;NDUFA1;UQCR11;NDUFC1;COX7A2;COX6C;COX7C | 4.022118e-19 | 15605.480700 | GO_Biological_Process_2023 | 343.482759 | 0 | 0 | 10/68 | 1.856362e-20 | 1555 | ||
| 998 | mitochondrial ATP synthesis coupled electron transport (GO:0042775) | NDUFA8;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFA1;UQCR11;NDUFC1;COX6C;COX7C | 2.222780e-18 | 14685.170247 | GO_Biological_Process_2021 | 326.540984 | 0 | 0 | 10/71 | 2.944080e-20 | 1403 | ||
| 997 | aerobic electron transport chain (GO:0019646) | NDUFA8;UQCRB;NDUFA12;NDUFA4;COX4I1;NDUFA1;UQCR11;NDUFC1;COX6C;COX7C | 2.222780e-18 | 14980.922968 | GO_Biological_Process_2021 | 332.000000 | 0 | 0 | 10/70 | 2.530558e-20 | 1402 | ||
| 894 | Striatum RNA M22 Module (Darkgreen) | NDUFA8;UQCRB;NDUFA12;NDUFA4;COX4I1;POMP;UQCR11;NDUFC1;COX7A2;COX6C;COX7C;UBL5;CHCHD2;ATP5E;TOMM7 | 1.594720e-16 | 4175.088817 | HDSigDB_Mouse_2021 | 97.234114 | 0 | 0 | 15/613 | 2.249253e-19 | 683 |
In [231]:
# df_degs = out_edgr_old_sf_v_gc.copy()
# df_degs = out_edgr_batches_snc_top.copy()
# df_degs = res_snc_grps.copy()
# factor_name = col_celltype
# factor_name = col_condition
factor_name = col_batch
df_degs = res_rank_genes_top.loc[:, "Overall", :]
df_degs = df_degs.assign(log_fc=df_degs.logfoldchanges.abs())
df_degs = df_degs.assign(abs_log_fc=df_degs.logfoldchanges_abs)
names = gp.get_library_name()
# [i for i in names if "brain" in i.lower() or "mouse" in i.lower()]
pathways_snc_vs_no, res_pathways_snc_vs_no = {}, {}
thresh_p_path = 0.05
# perts_geo = ["Aging", "Drug", "Disease"]
perts_geo = ["Aging", "Disease"]
other_sets = ["GTEx_Aging_Signatures_2021", f"HDSigDB_{species}_2021",
# "DGIdb_Drug_Targets_2024",
"MSigDB_Hallmark_2020",
"MSigDB_Oncogenic_Signatures", "OMIM_Disease",
# "Reactome_Pathways_2024", "Reactome_2022",
f"WikiPathways_2024_{species}", f"WikiPathways_2019_{species}"]
other_sets += [i for i in names if "GO_Biological_Process" in i]
# if species.lower() == "mouse":
# other_sets += ["PerturbAtlas_MouseGenePerturbationSigs"]
for x in df_degs.reset_index()[factor_name].unique():
pathways_snc_vs_no[x], res_pathways_snc_vs_no[x] = {}, {}
for i in ["up", "down"]:
degs_tmp = df_degs.loc[x]
if i == "up":
degs_tmp = list(degs_tmp[degs_tmp.log_fc > 0].index.values)
else:
degs_tmp = list(degs_tmp[degs_tmp.log_fc < 0].index.values)
if len(degs_tmp) > 0:
g_s = [f"{u}_Perturbations_from_GEO_{i}" for u in perts_geo] + [
f"Disease_Signatures_from_GEO_{i}_2014"] + other_sets
g_s = [g for g in g_s if g in names]
pathways_snc_vs_no[x][i] = gp.enrichr(
gene_list=degs_tmp, gene_sets=g_s,
organism=species, cutoff=0.5)
res_pathways_snc_vs_no[x][i] = pathways_snc_vs_no[x][i].results[
pathways_snc_vs_no[x][i].results[
"Adjusted P-value"] < thresh_p_path]
res_pathways_snc_vs_no[x] = pd.concat(res_pathways_snc_vs_no[x],
names=["Direction"])
res_pathways_snc_vs_no = pd.concat(res_pathways_snc_vs_no,
names=[col_celltype])
res_pathways_snc_vs_no.loc[
:, "Term Short"] = res_pathways_snc_vs_no.Term.apply(
lambda x: re.sub("Expression Of ", "", re.sub(".*.xls", "", re.sub(
".*.XLSX", "",
re.sub(".*Supplementary Table [0-9]+[-]?", "", re.sub(
".*.xlsx.", "", re.sub(".*Supplementary Data ..", "", re.sub(
"PMID([0-9])+", "", re.sub(
"([0-9]+) years v ([0-9]+) years", "\\1 v \\2 yrs.",
re.sub("aging:([0-9]+)", "aging", re.sub(
"([0-9]+) months v ([0-9]+) mo(nths)?",
"\\1 v \\2 mo.", re.sub("SRP[0-9]+ ", "", re.sub(
"GSE[0-9]+ ", "", x)))))))))))))
cts_i = res_pathways_snc_vs_no.reset_index()[col_celltype].unique()
res_pathways_snc_top_vs_no = res_pathways_snc_vs_no.reset_index().groupby([
col_celltype, "Direction"]).apply(lambda x: x.sort_values(
"Adjusted P-value", ascending=True).head(10), include_groups=False)
fig, axes = plt.subplots(*scflow.pl.square_grid(cts_i), figsize=(
20, 25), gridspec_kw=dict(wspace=1.5, top=0.95), squeeze=False)
for i, x in enumerate(cts_i):
gp.dotplot(
res_pathways_snc_top_vs_no.loc[x].reset_index(),
column="Adjusted P-value",
# column="Overlap",
x="Direction", y="Term Short",
ax=axes.flatten()[i], size=20, top_term=100,
title=x, xticklabels_rot=45, yticklabels_rot=45,
show_ring=True, marker="o", cutoff=0.5)
axes.flatten()[i].tick_params(axis="y", labelsize=8,
labelfontfamily="serif")
axes.flatten()[i].tick_params(axis="x", labelsize=10,
labelfontfamily="serif")
axes.flatten()[i].title.set_fontsize(10)
if len(axes.flatten()) > len(cts_i):
for a in axes.flatten()[len(cts_i):]:
a.set_visible(False)
fig.suptitle("Senescent Cell Spaceflight DEGs",
fontsize="xx-large",
fontproperties=dict(family="serif"))
res_pathways_snc_top_vs_no[["Term Short", "Genes"] + list(
res_pathways_snc_top_vs_no.columns.difference([
"Term", "Genes", "Term Short"]))]
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms))
Out[231]:
| Term Short | Genes | Adjusted P-value | Combined Score | Gene_set | Odds Ratio | Old Adjusted P-value | Old P-value | Overlap | P-value | level_2 | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| annotation_by_overlap | Direction | ||||||||||||
| Ground Control | 12 Weeks | up | 6 | Genes Changed In iSPNs Of R6/2 Vs WT | GRID1;SGIP1;NTM;ATP2B2;ANK2;GRIN2B;DLG2;APC;MMP16;CDH10;B3GALT1;KCNMA1;GRIA3;DYNC1I1;DGKI | 9.429651e-14 | 2368.732160 | HDSigDB_Mouse_2021 | 63.972067 | 0 | 0 | 15/910 | 8.300749e-17 | 261 |
| 7 | Genes Changed In dSPNs Of R6/2 Vs WT | GRID1;SGIP1;NTM;ATP2B2;ANK2;GRIN2B;APC;MMP16;CDH10;B3GALT1;KCNMA1;GRIA3;DYNC1I1;DGKI | 1.615311e-11 | 1343.117546 | HDSigDB_Mouse_2021 | 43.061019 | 0 | 0 | 14/1041 | 2.843857e-14 | 262 | ||
| 8 | Genes Changed In Foxp2/Olfm3-expressing Neurons Of R6/2 Vs WT | GRID1;SGIP1;NTM;ATP2B2;ANK2;GRIN2B;TANC2;APC;CDH10;B3GALT1;HIVEP3;GRIA3;DYNC1I1 | 2.581291e-11 | 1303.947210 | HDSigDB_Mouse_2021 | 43.010710 | 0 | 0 | 13/840 | 6.816789e-14 | 263 | ||
| 9 | Up-regulated Genes In Cerebellum Of 6 Mon HD Q140 Mice Vs Q20 GSE78270 | GRID1;SGIP1;ATP2B2;ANK2;NOL4;GRIN2B;TANC2;DLG2;APC;B3GALT1;KCNMA1;HIVEP3;AMPH;GRIA3;DYNC1I1 | 2.798419e-11 | 1145.601891 | HDSigDB_Mouse_2021 | 38.252581 | 0 | 0 | 15/1468 | 9.853589e-14 | 264 | ||
| 11 | Top Neuron-Expressed Genes In Humans And Mice | DYNC1I2;SGIP1;NTM;ATP2B2;ANK2;GRIN2B;TANC2;DLG2;APC;KCNMA1;AMPH;GRIA3;DYNC1I1 | 2.254786e-10 | 930.498937 | HDSigDB_Mouse_2021 | 33.890173 | 0 | 0 | 13/1051 | 1.190908e-12 | 266 | ||
| 10 | Genes Changed In iSPNs Of zQ175DN Vs WT | DLG2;MMP16;SGIP1;NTM;CDH10;B3GALT1;ANK2;HIVEP3;GRIN2B;GRIA3;DYNC1I1 | 2.254786e-10 | 1185.862385 | HDSigDB_Mouse_2021 | 43.016908 | 0 | 0 | 11/563 | 1.065736e-12 | 265 | ||
| 12 | Genes Changed In Chat-expressing Interneurons Of R6/2 Vs WT | GRID1;NOL4;GRIN2B;TANC2;DLG2;APC;MMP16;B3GALT1;HIVEP3;AMPH;GRIA3;DYNC1I1;DGKI | 5.485884e-10 | 819.807501 | HDSigDB_Mouse_2021 | 31.037994 | 0 | 0 | 13/1141 | 3.380386e-12 | 267 | ||
| 13 | Genes Up-Regulated In Control iPSCs After 60 D Of Differentiation GSE109534 | DYNC1I2;GRID1;SGIP1;NTM;ATP2B2;ANK2;NOL4;GRIN2B;TANC2;DLG2;APC;CDH10;KCNMA1;HIVEP3;AMPH;GRIA3;DY... | 6.402671e-10 | 1083.738559 | HDSigDB_Mouse_2021 | 41.482875 | 0 | 0 | 18/3580 | 4.508923e-12 | 268 | ||
| 14 | Genes Up-Regulated In Sst/Npy-expressing Interneurons Of R6/2 Vs WT | DLG2;APC;MMP16;CDH10;B3GALT1;KCNMA1;ANK2;NOL4;TANC2 | 1.212627e-09 | 1299.964101 | HDSigDB_Mouse_2021 | 51.243196 | 0 | 0 | 9/323 | 9.607078e-12 | 269 | ||
| 15 | Top Neuron-Enriched Genes In Humans And Mice | DLG2;MMP16;SGIP1;CDH10;B3GALT1;KCNMA1;ATP2B2;AMPH;NOL4;GRIN2B;GRIA3;DYNC1I1 | 2.780780e-09 | 691.246334 | HDSigDB_Mouse_2021 | 28.291252 | 0 | 0 | 12/1018 | 2.447870e-11 | 270 | ||
| Ground Control | 29 Weeks | up | 507 | Negative Regulation Of Peptidase Activity (GO:0010466) | SERPINE2;TIMP2;NRG1;CTSB | 9.062129e-05 | 1324.283459 | GO_Biological_Process_2023 | 88.946429 | 0 | 0 | 4/60 | 3.419671e-07 | 2017 |
| 508 | Negative Regulation Of Proteolysis (GO:0045861) | SERPINE2;TIMP2;CTSB | 1.178829e-03 | 1077.036200 | GO_Biological_Process_2023 | 92.609907 | 0 | 0 | 3/41 | 8.896824e-06 | 2018 | ||
| 510 | Negative Regulation Of Endopeptidase Activity (GO:0010951) | SERPINE2;TIMP2;NRG1 | 1.897170e-03 | 568.434094 | GO_Biological_Process_2023 | 55.789916 | 0 | 0 | 3/66 | 3.758775e-05 | 2020 | ||
| 509 | Negative Regulation Of Platelet Aggregation (GO:0090331) | SERPINE2;CD9 | 1.897170e-03 | 3897.964407 | GO_Biological_Process_2023 | 369.888889 | 0 | 0 | 2/8 | 2.650430e-05 | 2019 | ||
| 512 | Regulation Of Endopeptidase Activity (GO:0052548) | SERPINE2;TIMP2;NRG1 | 1.897170e-03 | 535.407712 | GO_Biological_Process_2023 | 53.245989 | 0 | 0 | 3/69 | 4.295480e-05 | 2022 | ||
| 511 | Negative Regulation Of Homotypic Cell-Cell Adhesion (GO:0034111) | SERPINE2;CD9 | 1.897170e-03 | 2791.902845 | GO_Biological_Process_2023 | 277.388889 | 0 | 0 | 2/10 | 4.254530e-05 | 2021 | ||
| 513 | Negative Regulation Of Platelet Activation (GO:0010544) | SERPINE2;CD9 | 3.746939e-03 | 1573.576348 | GO_Biological_Process_2023 | 170.658120 | 0 | 0 | 2/15 | 9.897575e-05 | 2023 | ||
| 514 | Heart Trabecula Morphogenesis (GO:0061384) | S1PR1;NRG1 | 4.768772e-03 | 1226.393330 | GO_Biological_Process_2023 | 138.638889 | 0 | 0 | 2/18 | 1.439629e-04 | 2024 | ||
| 475 | Status Epilepticus C0038220 rat sample 391 | SERPINE2;VGF;TIMP2;CD9;CTSB | 5.809842e-03 | 218.064714 | Disease_Perturbations_from_GEO_up | 19.787513 | 0 | 0 | 5/336 | 1.636575e-05 | 146 | ||
| 517 | Negative Regulation of Platelet Aggregation (GO:0090331) | SERPINE2;CD9 | 5.846481e-03 | 3261.459893 | GO_Biological_Process_2025 | 317.031746 | 0 | 0 | 2/9 | 3.405660e-05 | 2282 | ||
| Space Flight | 12 Weeks | up | 530 | Genes Changed In Cerebellum Of 12 Week Old Atxn1[154Q/2Q] Vs WT GSE122099 | KCNJ6;AIFM3;VGF;TIMP2;ADCY2;SLC6A11;HIVEP3;MIF;HMGB1;KALRN;KCNH1 | 1.440888e-03 | 129.867923 | HDSigDB_Mouse_2021 | 9.867696 | 0 | 0 | 11/2213 | 1.924361e-06 | 558 |
| 531 | Genes Up-Regulated In Pvalb/Th-expressing Interneurons Of HD Patients Vs Control | KCNJ6;FAM135B;TIMP2;FMN1;ADCY2;HMGB1;PBX1 | 1.440888e-03 | 202.340571 | HDSigDB_Mouse_2021 | 15.664041 | 0 | 0 | 7/671 | 2.454664e-06 | 559 | ||
| 533 | Yellow Module Eigengene Expression Profile Across Striatal Astrocytes Of R6/2 And Q175 Mice | NEUROD2;KCNJ6;FAM135B;VGF;9130024F11RIK;KALRN;KCNH1 | 1.866948e-03 | 161.172389 | HDSigDB_Mouse_2021 | 13.469952 | 0 | 0 | 7/775 | 6.360982e-06 | 561 | ||
| 532 | Mouse Glia-Enriched Genes | NEUROD2;KCNJ6;AIFM3;MICAL2;9130024F11RIK;FMN1;ADCY2;SLC6A11;HIVEP3 | 1.866948e-03 | 123.862865 | HDSigDB_Mouse_2021 | 10.219815 | 0 | 0 | 9/1490 | 5.450114e-06 | 560 | ||
| 534 | Genes Up-Regulated In Brain Vs Spinal Cord Derived OPCs From P7 Mice | NEUROD2;KCNJ6;AIFM3;VGF;MICAL2;9130024F11RIK;KCNH1 | 2.378468e-03 | 143.774537 | HDSigDB_Mouse_2021 | 12.502098 | 0 | 0 | 7/832 | 1.012976e-05 | 562 | ||
| 535 | Genes Down-Regulated In The Cortex Of zQ175 Neo- Mice Vs WT GSE123657 | AIFM3;MICAL2;FMN1;ADCY2;KALRN;KCNH1 | 5.531236e-03 | 133.588136 | HDSigDB_Mouse_2021 | 12.847176 | 0 | 0 | 6/651 | 3.048582e-05 | 563 | ||
| 537 | Top Neuron-Specific Genes In Humans And Mice | NEUROD2;KCNJ6;FAM135B;VGF;MICAL2;KALRN;KCNH1 | 5.531236e-03 | 102.802236 | HDSigDB_Mouse_2021 | 10.092429 | 0 | 0 | 7/1019 | 3.769156e-05 | 565 | ||
| 536 | Top Neuron-Enriched Genes In Humans And Mice | KCNJ6;FAM135B;VGF;MICAL2;ADCY2;KALRN;KCNH1 | 5.531236e-03 | 102.973029 | HDSigDB_Mouse_2021 | 10.102945 | 0 | 0 | 7/1018 | 3.745472e-05 | 564 | ||
| 524 | Malignant mesothelioma of pleura C0812413 human sample 118 | MICAL2;MIF;HMGB1;PBX1;CTSB | 1.216834e-02 | 170.230032 | Disease_Perturbations_from_GEO_up | 16.656463 | 0 | 0 | 5/397 | 3.643214e-05 | 129 | ||
| 538 | Genes Changed In Pvalb/Th-expressing Interneurons Of R6/2 Vs WT | FAM135B;MICAL2;HIVEP3;KALRN;PBX1;KCNH1 | 1.297377e-02 | 92.699828 | HDSigDB_Mouse_2021 | 10.142857 | 0 | 0 | 6/816 | 1.073496e-04 | 566 | ||
| Space Flight | 29 Weeks | up | 572 | Genes Up-Regulated In Endothelial Cells Of zQ175DN Vs WT | DST;PCDH9;NAV3;MAGI2;MYCBP2;KCNQ3;UBR3;CCSER1;MDGA2;GPHN;PTK2 | 1.535462e-10 | 1549.174518 | HDSigDB_Mouse_2021 | 52.330409 | 0 | 0 | 11/467 | 1.390817e-13 | 348 |
| 573 | Genes Changed In Foxp2/Olfm3-expressing Neurons Of R6/2 Vs WT | PCDH9;NBEA;NAV3;DENND1A;MAGI2;KCNQ3;CCSER1;PRICKLE1;MDGA2;KALRN;GPHN;STXBP5L | 1.421789e-09 | 925.849932 | HDSigDB_Mouse_2021 | 34.695652 | 0 | 0 | 12/840 | 2.575705e-12 | 349 | ||
| 574 | Genes Changed In Pvalb/Th-expressing Interneurons Of R6/2 Vs WT | PCLO;PCDH9;NAV3;DENND1A;MAGI2;MYCBP2;KCNQ3;CCSER1;MDGA2;KALRN;STXBP5L | 1.991572e-08 | 685.957702 | HDSigDB_Mouse_2021 | 29.113182 | 0 | 0 | 11/816 | 5.851390e-11 | 350 | ||
| 575 | Genes Changed In Ciliated Ependymal Cells Of HD Patients Vs Control | DST;PCDH9;DENND1A;MAGI2;MYCBP2;UBR3;KALRN;GPHN;PTK2;STXBP5L;PCLO;NBEA;NAV3;AKT3;CCSER1;MDGA2 | 1.991572e-08 | 567.464042 | HDSigDB_Mouse_2021 | 24.300283 | 0 | 0 | 16/2840 | 7.215839e-11 | 351 | ||
| 576 | Genes Changed In Foxp2/Olfm3-expressing Neurons Of zQ175DN Vs WT | PCLO;PCDH9;NBEA;DENND1A;MAGI2;CCSER1;MDGA2;GPHN;PTK2;STXBP5L | 2.880483e-08 | 707.166832 | HDSigDB_Mouse_2021 | 31.070626 | 0 | 0 | 10/633 | 1.304566e-10 | 352 | ||
| 577 | Genes Changed In iSPNs Of R6/2 Vs WT | PCDH9;NBEA;NAV3;DENND1A;MAGI2;KCNQ3;CCSER1;MDGA2;KALRN;GPHN;STXBP5L | 3.455452e-08 | 580.972458 | HDSigDB_Mouse_2021 | 25.941293 | 0 | 0 | 11/910 | 1.877963e-10 | 353 | ||
| 578 | Genes Changed In Endothelial Cells Of zQ175DN Vs WT | DST;PCDH9;NAV3;MAGI2;MYCBP2;KCNQ3;UBR3;CCSER1;MDGA2;GPHN;PTK2 | 4.899421e-08 | 540.168621 | HDSigDB_Mouse_2021 | 24.673854 | 0 | 0 | 11/954 | 3.106517e-10 | 354 | ||
| 579 | Genes Up-Regulated In Sst/Npy-expressing Interneurons Of R6/2 Vs WT | PCLO;DST;PCDH9;NAV3;DENND1A;MAGI2;MYCBP2;MDGA2 | 6.232223e-08 | 895.567048 | HDSigDB_Mouse_2021 | 41.619048 | 0 | 0 | 8/323 | 4.516103e-10 | 355 | ||
| 580 | Genes Changed In dSPNs Of R6/2 Vs WT | PCLO;PCDH9;NBEA;NAV3;DENND1A;MAGI2;KCNQ3;CCSER1;MDGA2;KALRN;STXBP5L | 9.632781e-08 | 471.429338 | HDSigDB_Mouse_2021 | 22.486516 | 0 | 0 | 11/1041 | 7.852810e-10 | 356 | ||
| 581 | Genes Up-Regulated In Control iPSCs After 60 D Of Differentiation GSE109534 | PCDH9;MAGI2;MYCBP2;UBR3;KALRN;GPHN;STXBP5L;PCLO;NBEA;NAV3;KCNQ1OT1;AKT3;KCNQ3;MALAT1;CCSER1;MDGA2 | 2.773367e-07 | 364.839499 | HDSigDB_Mouse_2021 | 18.424242 | 0 | 0 | 16/3580 | 2.512107e-09 | 357 |
In [232]:
# df_degs = out_edgr_old_sf_v_gc.copy()
# df_degs = out_edgr_batches_snc_top.copy()
# df_degs = res_snc_grps.copy()
factor_name = col_celltype
# factor_name = col_condition
iii = 2
df_degs = res_rank_genes_top.loc[self.rna.obs[col_batch].unique()[iii]]
df_degs = df_degs.assign(log_fc=df_degs.logfoldchanges.abs())
df_degs = df_degs.assign(abs_log_fc=df_degs.logfoldchanges_abs)
print(self.rna.obs[col_batch].unique()[iii])
names = gp.get_library_name()
# [i for i in names if "brain" in i.lower() or "mouse" in i.lower()]
pathways_snc_vs_no, res_pathways_snc_vs_no = {}, {}
thresh_p_path = 0.05
# perts_geo = ["Aging", "Drug", "Disease"]
perts_geo = ["Aging", "Disease"]
other_sets = ["GTEx_Aging_Signatures_2021", f"HDSigDB_{species}_2021",
# "DGIdb_Drug_Targets_2024",
"MSigDB_Hallmark_2020",
"MSigDB_Oncogenic_Signatures", "OMIM_Disease",
# "Reactome_Pathways_2024", "Reactome_2022",
f"WikiPathways_2024_{species}", f"WikiPathways_2019_{species}"]
other_sets += [i for i in names if "GO_Biological_Process" in i]
# if species.lower() == "mouse":
# other_sets += ["PerturbAtlas_MouseGenePerturbationSigs"]
for x in df_degs.reset_index()[factor_name].unique():
pathways_snc_vs_no[x], res_pathways_snc_vs_no[x] = {}, {}
for i in ["up", "down"]:
degs_tmp = df_degs.loc[x]
if i == "up":
degs_tmp = list(degs_tmp[degs_tmp.log_fc > 0].index.values)
else:
degs_tmp = list(degs_tmp[degs_tmp.log_fc < 0].index.values)
if len(degs_tmp) > 0:
g_s = [f"{u}_Perturbations_from_GEO_{i}" for u in perts_geo] + [
f"Disease_Signatures_from_GEO_{i}_2014"] + other_sets
g_s = [g for g in g_s if g in names]
pathways_snc_vs_no[x][i] = gp.enrichr(
gene_list=degs_tmp, gene_sets=g_s,
organism=species, cutoff=0.5)
res_pathways_snc_vs_no[x][i] = pathways_snc_vs_no[x][i].results[
pathways_snc_vs_no[x][i].results[
"Adjusted P-value"] < thresh_p_path]
res_pathways_snc_vs_no[x] = pd.concat(res_pathways_snc_vs_no[x],
names=["Direction"])
res_pathways_snc_vs_no = pd.concat(res_pathways_snc_vs_no,
names=[col_celltype])
res_pathways_snc_vs_no.loc[
:, "Term Short"] = res_pathways_snc_vs_no.Term.apply(
lambda x: re.sub("Expression Of ", "", re.sub(".*.xls", "", re.sub(
".*.XLSX", "",
re.sub(".*Supplementary Table [0-9]+[-]?", "", re.sub(
".*.xlsx.", "", re.sub(".*Supplementary Data ..", "", re.sub(
"PMID([0-9])+", "", re.sub(
"([0-9]+) years v ([0-9]+) years", "\\1 v \\2 yrs.",
re.sub("aging:([0-9]+)", "aging", re.sub(
"([0-9]+) months v ([0-9]+) mo(nths)?",
"\\1 v \\2 mo.", re.sub("SRP[0-9]+ ", "", re.sub(
"GSE[0-9]+ ", "", x)))))))))))))
cts_i = res_pathways_snc_vs_no.reset_index()[col_celltype].unique()
res_pathways_snc_top_vs_no = res_pathways_snc_vs_no.reset_index().groupby([
col_celltype, "Direction"]).apply(lambda x: x.sort_values(
"Adjusted P-value", ascending=True).head(10), include_groups=False)
fig, axes = plt.subplots(*scflow.pl.square_grid(cts_i), figsize=(
20, 25), gridspec_kw=dict(wspace=1.5, top=0.95), squeeze=False)
for i, x in enumerate(cts_i):
gp.dotplot(
res_pathways_snc_top_vs_no.loc[x].reset_index(),
column="Adjusted P-value",
# column="Overlap",
x="Direction", y="Term Short",
ax=axes.flatten()[i], size=20, top_term=100,
title=x, xticklabels_rot=45, yticklabels_rot=45,
show_ring=True, marker="o", cutoff=0.5)
axes.flatten()[i].tick_params(axis="y", labelsize=8,
labelfontfamily="serif")
axes.flatten()[i].tick_params(axis="x", labelsize=10,
labelfontfamily="serif")
axes.flatten()[i].title.set_fontsize(10)
if len(axes.flatten()) > len(cts_i):
for a in axes.flatten()[len(cts_i):]:
a.set_visible(False)
fig.suptitle("Senescent Cell Spaceflight DEGs", fontsize="xx-large",
fontproperties=dict(family="serif"))
res_pathways_snc_top_vs_no[["Term Short", "Genes"] + list(
res_pathways_snc_top_vs_no.columns.difference([
"Term", "Genes", "Term Short"]))]
Space Flight | 29 Weeks
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms))
Out[232]:
| Term Short | Genes | Adjusted P-value | Combined Score | Gene_set | Odds Ratio | Old Adjusted P-value | Old P-value | Overlap | P-value | level_2 | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| annotation_by_overlap | Direction | ||||||||||||
| Excitatory | up | 5 | Genes Up-Regulated In Oligodendrocytes Of zQ175DN Vs WT | TENM2;SYT1;NEGR1;ADGRB3;CELF2;NRXN1;CTNND2;CSMD1;FGF12 | 9.868939e-16 | 14425.378650 | HDSigDB_Mouse_2021 | 346.996132 | 0 | 0 | 9/56 | 8.819427e-19 | 345 |
| 6 | Genes Up-Regulated In Microglial Cells Of zQ175DN Vs WT | TENM2;SYT1;NEGR1;ADGRB3;NRXN1;CTNND2;CSMD1;FGF12 | 4.901372e-14 | 11987.300746 | HDSigDB_Mouse_2021 | 324.211382 | 0 | 0 | 8/49 | 8.760272e-17 | 346 | ||
| 7 | Genes Up-Regulated In Astrocytes Of zQ175DN Vs WT | TENM2;SYT1;NEGR1;ADGRB3;CELF2;KCNQ3;ERC2;CSMD1 | 1.382226e-13 | 9441.901030 | HDSigDB_Mouse_2021 | 265.733333 | 0 | 0 | 8/58 | 3.705701e-16 | 347 | ||
| 8 | Genes Changed In Microglial Cells Of zQ175DN Vs WT | TENM2;SYT1;NEGR1;ADGRB3;NRXN1;CTNND2;CSMD1;FGF12 | 4.495854e-13 | 7415.603564 | HDSigDB_Mouse_2021 | 217.693989 | 0 | 0 | 8/69 | 1.607097e-15 | 348 | ||
| 9 | Genes Changed In Oligodendrocytes Of zQ175DN Vs WT | TENM2;SYT1;NEGR1;ADGRB3;CELF2;NRXN1;CTNND2;CSMD1;FGF12 | 6.366682e-13 | 4424.009348 | HDSigDB_Mouse_2021 | 132.086475 | 0 | 0 | 9/132 | 2.844809e-15 | 349 | ||
| 10 | Genes Changed In iSPNs Of zQ175DN Vs WT | AHI1;CAMK1D;SYT1;NEGR1;ADGRB3;CELF2;NRXN1;B3GALT1;SORBS2;ERC2;DIP2C;GPR158 | 4.218271e-12 | 1661.868528 | HDSigDB_Mouse_2021 | 52.892015 | 0 | 0 | 12/563 | 2.261807e-14 | 350 | ||
| 11 | Genes Changed In dSPNs Of R6/2 Vs WT | CAMK1D;SYT1;NEGR1;CELF2;NRXN1;SORBS2;PCLO;ADGRB3;B3GALT1;KCNQ3;ERC2;CSMD1;FGF12;GPR158 | 4.546108e-12 | 1343.117546 | HDSigDB_Mouse_2021 | 43.061019 | 0 | 0 | 14/1041 | 2.843857e-14 | 351 | ||
| 12 | Genes Changed In Astrocytes Of zQ175DN Vs WT | TENM2;SYT1;NEGR1;ADGRB3;CELF2;KCNQ3;ERC2;CSMD1 | 4.715813e-12 | 4464.460737 | HDSigDB_Mouse_2021 | 144.115942 | 0 | 0 | 8/100 | 3.518145e-14 | 352 | ||
| 13 | Genes Down-Regulated In Foxp2/Olfm3-expressing Neurons Of R6/2 Vs WT | AHI1;CAMK1D;SYT1;NEGR1;ADGRB3;CELF2;B3GALT1;KCNQ3;SORBS2;ERC2;DIP2C;GPR158 | 4.715813e-12 | 1561.570553 | HDSigDB_Mouse_2021 | 50.531250 | 0 | 0 | 12/588 | 3.792879e-14 | 353 | ||
| 14 | Genes Down-Regulated In iSPNs Of zQ175DN Vs WT | AHI1;CAMK1D;SYT1;NEGR1;ADGRB3;CELF2;B3GALT1;SORBS2;ERC2;DIP2C;GPR158 | 5.696360e-12 | 1763.714254 | HDSigDB_Mouse_2021 | 57.621151 | 0 | 0 | 11/426 | 5.090581e-14 | 354 | ||
| Excitatory-Inhibitory | up | 411 | dementia | MAPT | 2.861721e-02 | 423.083052 | OMIM_Disease | 95.545455 | 0 | 0 | 1/12 | 1.193737e-02 | 940 |
| 412 | parkinson disease | MAPT | 2.861721e-02 | 191.420591 | OMIM_Disease | 50.022556 | 0 | 0 | 1/22 | 2.178169e-02 | 941 | ||
| 413 | hypertension | NR3C2 | 2.861721e-02 | 133.278878 | OMIM_Disease | 37.503759 | 0 | 0 | 1/29 | 2.861721e-02 | 942 | ||
| 415 | Stress Granule Assembly (GO:0034063) | G3BP2;MAPT | 3.851057e-02 | 788.330757 | GO_Biological_Process_2025 | 96.410628 | 0 | 0 | 2/25 | 2.810990e-04 | 1266 | ||
| 414 | Stress Granule Assembly (GO:0034063) | G3BP2;MAPT | 4.010875e-02 | 832.549579 | GO_Biological_Process_2023 | 100.797980 | 0 | 0 | 2/24 | 2.587661e-04 | 1111 | ||
| Inhibitory | up | 416 | Mouse Glia-Enriched Genes | PLEKHG3;SPSB1;ASAH2;CCDC15;EYA4;DKKL1;CORO6;PACSIN3;HR;PARVA;BHLHE22 | 3.229224e-05 | 262.974230 | HDSigDB_Mouse_2021 | 15.288934 | 0 | 0 | 11/1490 | 3.388482e-08 | 206 |
| 417 | Fatty Acid Oxidation WP2318 | CPT2 | 4.356338e-02 | 538.335775 | WikiPathways_2024_Mouse | 116.789474 | 0 | 0 | 1/10 | 9.957248e-03 | 1200 | ||
| 418 | Mitochondrial Long Chain Fatty Acid Beta Oxidation WP401 | CPT2 | 4.356338e-02 | 290.178721 | WikiPathways_2024_Mouse | 70.052632 | 0 | 0 | 1/16 | 1.588634e-02 | 1201 | ||
| 419 | Signal Transduction Of S1P Receptor WP57 | ASAH2 | 4.356338e-02 | 191.420591 | WikiPathways_2024_Mouse | 50.022556 | 0 | 0 | 1/22 | 2.178169e-02 | 1202 | ||
| 420 | Fatty acid oxidation WP2318 | CPT2 | 4.356338e-02 | 538.335775 | WikiPathways_2019_Mouse | 116.789474 | 0 | 0 | 1/10 | 9.957248e-03 | 1206 | ||
| 421 | Mitochondrial LC-Fatty Acid Beta-Oxidation WP401 | CPT2 | 4.356338e-02 | 290.178721 | WikiPathways_2019_Mouse | 70.052632 | 0 | 0 | 1/16 | 1.588634e-02 | 1207 | ||
| 422 | Signal Transduction of S1P Receptor WP57 | ASAH2 | 4.356338e-02 | 191.420591 | WikiPathways_2019_Mouse | 50.022556 | 0 | 0 | 1/22 | 2.178169e-02 | 1208 | ||
| Microglial | up | 428 | Top Oligodendrocyte-Enriched Genes In Humans And Mice | TMOD1;FA2H;HIP1;GLRB;PLEKHB1;ENDOD1;CCP110;MRPL48;SLAIN1;PAKAP | 1.810149e-05 | 332.952999 | HDSigDB_Mouse_2021 | 18.492683 | 0 | 0 | 10/1035 | 1.516038e-08 | 425 |
| 429 | Down-regulated Genes In Cortex Of 10 Mon HD Q140 Mice Vs Q20 GSE78272 | TMOD1;FA2H;HIP1;DTNA;SEMA6D;CCP110;SLAIN1 | 3.844163e-05 | 454.994651 | HDSigDB_Mouse_2021 | 27.478365 | 0 | 0 | 7/391 | 6.439134e-08 | 426 | ||
| 430 | Differentially Expressed Genes In Cortex Of 10 Mon HD Q140 Mice Vs Q20 GSE78272 | TMOD1;FA2H;HIP1;DTNA;SEMA6D;CCP110;SLAIN1;PCYOX1 | 5.981462e-05 | 300.934955 | HDSigDB_Mouse_2021 | 19.154762 | 0 | 0 | 8/680 | 1.502880e-07 | 427 | ||
| 431 | Oligodendrocyte Gene Module From Human Brain | FA2H;HIP1;PLEKHB1;ENDOD1;CCP110;MRPL48;KIF3C | 4.567290e-04 | 225.893209 | HDSigDB_Mouse_2021 | 16.870052 | 0 | 0 | 7/625 | 1.530081e-06 | 428 | ||
| 432 | Genes Up-Regulated In Pons Of 28 Week WT Mice Vs Atxn 1 154Q/2Q Mice Both Treated With Vehicle G... | TMOD1;FA2H;GLRB;SEMA6D;PLEKHB1;ENDOD1;CCP110;MRPL48;SLAIN1;KIF3C | 1.134028e-03 | 116.302476 | HDSigDB_Mouse_2021 | 9.488189 | 0 | 0 | 10/1915 | 4.748863e-06 | 429 | ||
| 433 | Genes Changed In Striatum Of R6/2 Vs WT Mice GSE109416 | TMOD1;DTNA;SEMA6D;PLEKHB1;ENDOD1;CCP110;MRPL48;SYNE1;PAKAP;FA2H;SIN3B;PUF60;C130023A14RIK;KIF3C | 1.188821e-03 | 99.871906 | HDSigDB_Mouse_2021 | 8.303217 | 0 | 0 | 14/4397 | 5.973976e-06 | 430 | ||
| 434 | Genes Changed In Pons Of 28 Week Old WT Mice Vs Atxn 1 154Q/2Q Mice Both Treated With Vehicle GS... | TMOD1;FA2H;SIN3B;GLRB;PPME1;SEMA6D;PLEKHB1;ENDOD1;CCP110;MRPL48;SLAIN1;KIF3C | 3.626597e-03 | 76.956862 | HDSigDB_Mouse_2021 | 7.189475 | 0 | 0 | 12/3461 | 2.245269e-05 | 431 | ||
| 435 | Oligodendrocyte Gene Module From Cerebral Cortex | TMOD1;FA2H;HIP1;PLEKHB1;ENDOD1;CCP110;KIF3C | 3.626597e-03 | 110.537796 | HDSigDB_Mouse_2021 | 10.564182 | 0 | 0 | 7/976 | 2.856151e-05 | 432 | ||
| 437 | Mouse Cortex Mature Oligodendrocyte Markers | FA2H;HIP1;PUF60;SEMA6D;PLEKHB1;ENDOD1;CCP110;MRPL48;SLAIN1 | 3.626597e-03 | 84.358435 | HDSigDB_Mouse_2021 | 8.109875 | 0 | 0 | 9/1840 | 3.037351e-05 | 434 | ||
| 436 | Down-regulated Genes In Cerebellum Of 6 Mon HD Q175 Mice Vs Q20 GSE73468 | FA2H;GLRB;PLEKHB1;ENDOD1;CCP110;MRPL48;SLAIN1;PAKAP | 3.626597e-03 | 94.424433 | HDSigDB_Mouse_2021 | 9.055961 | 0 | 0 | 8/1378 | 2.962858e-05 | 433 | ||
| Oligodendrocyte | up | 501 | Cancer of prostate GSE1413 | CD9;MLLT1;SNX10;S100A10 | 6.761483e-03 | 141.692774 | Disease_Signatures_from_GEO_up_2014 | 16.625000 | 0 | 0 | 4/300 | 1.988671e-04 | 393 |
| Overall | up | 503 | Genes Up-Regulated In Endothelial Cells Of zQ175DN Vs WT | DST;PCDH9;NAV3;MAGI2;MYCBP2;KCNQ3;UBR3;CCSER1;MDGA2;GPHN;PTK2 | 1.535462e-10 | 1549.174518 | HDSigDB_Mouse_2021 | 52.330409 | 0 | 0 | 11/467 | 1.390817e-13 | 348 |
| 504 | Genes Changed In Foxp2/Olfm3-expressing Neurons Of R6/2 Vs WT | PCDH9;NBEA;NAV3;DENND1A;MAGI2;KCNQ3;CCSER1;PRICKLE1;MDGA2;KALRN;GPHN;STXBP5L | 1.421789e-09 | 925.849932 | HDSigDB_Mouse_2021 | 34.695652 | 0 | 0 | 12/840 | 2.575705e-12 | 349 | ||
| 505 | Genes Changed In Pvalb/Th-expressing Interneurons Of R6/2 Vs WT | PCLO;PCDH9;NAV3;DENND1A;MAGI2;MYCBP2;KCNQ3;CCSER1;MDGA2;KALRN;STXBP5L | 1.991572e-08 | 685.957702 | HDSigDB_Mouse_2021 | 29.113182 | 0 | 0 | 11/816 | 5.851390e-11 | 350 | ||
| 506 | Genes Changed In Ciliated Ependymal Cells Of HD Patients Vs Control | DST;PCDH9;DENND1A;MAGI2;MYCBP2;UBR3;KALRN;GPHN;PTK2;STXBP5L;PCLO;NBEA;NAV3;AKT3;CCSER1;MDGA2 | 1.991572e-08 | 567.464042 | HDSigDB_Mouse_2021 | 24.300283 | 0 | 0 | 16/2840 | 7.215839e-11 | 351 | ||
| 507 | Genes Changed In Foxp2/Olfm3-expressing Neurons Of zQ175DN Vs WT | PCLO;PCDH9;NBEA;DENND1A;MAGI2;CCSER1;MDGA2;GPHN;PTK2;STXBP5L | 2.880483e-08 | 707.166832 | HDSigDB_Mouse_2021 | 31.070626 | 0 | 0 | 10/633 | 1.304566e-10 | 352 | ||
| 508 | Genes Changed In iSPNs Of R6/2 Vs WT | PCDH9;NBEA;NAV3;DENND1A;MAGI2;KCNQ3;CCSER1;MDGA2;KALRN;GPHN;STXBP5L | 3.455452e-08 | 580.972458 | HDSigDB_Mouse_2021 | 25.941293 | 0 | 0 | 11/910 | 1.877963e-10 | 353 | ||
| 509 | Genes Changed In Endothelial Cells Of zQ175DN Vs WT | DST;PCDH9;NAV3;MAGI2;MYCBP2;KCNQ3;UBR3;CCSER1;MDGA2;GPHN;PTK2 | 4.899421e-08 | 540.168621 | HDSigDB_Mouse_2021 | 24.673854 | 0 | 0 | 11/954 | 3.106517e-10 | 354 | ||
| 510 | Genes Up-Regulated In Sst/Npy-expressing Interneurons Of R6/2 Vs WT | PCLO;DST;PCDH9;NAV3;DENND1A;MAGI2;MYCBP2;MDGA2 | 6.232223e-08 | 895.567048 | HDSigDB_Mouse_2021 | 41.619048 | 0 | 0 | 8/323 | 4.516103e-10 | 355 | ||
| 511 | Genes Changed In dSPNs Of R6/2 Vs WT | PCLO;PCDH9;NBEA;NAV3;DENND1A;MAGI2;KCNQ3;CCSER1;MDGA2;KALRN;STXBP5L | 9.632781e-08 | 471.429338 | HDSigDB_Mouse_2021 | 22.486516 | 0 | 0 | 11/1041 | 7.852810e-10 | 356 | ||
| 512 | Genes Up-Regulated In Control iPSCs After 60 D Of Differentiation GSE109534 | PCDH9;MAGI2;MYCBP2;UBR3;KALRN;GPHN;STXBP5L;PCLO;NBEA;NAV3;KCNQ1OT1;AKT3;KCNQ3;MALAT1;CCSER1;MDGA2 | 2.773367e-07 | 364.839499 | HDSigDB_Mouse_2021 | 18.424242 | 0 | 0 | 16/3580 | 2.512107e-09 | 357 |
In [230]:
# df_degs = out_edgr_old_sf_v_gc.copy()
# df_degs = out_edgr_batches_snc_top.copy()
# df_degs = res_snc_grps.copy()
factor_name = col_celltype
# factor_name = col_condition
iii = 1
df_degs = res_rank_genes_top.loc[self.rna.obs[col_batch].unique()[iii]]
df_degs = df_degs.assign(log_fc=df_degs.logfoldchanges.abs())
df_degs = df_degs.assign(abs_log_fc=df_degs.logfoldchanges_abs)
print(self.rna.obs[col_batch].unique()[iii])
names = gp.get_library_name()
# [i for i in names if "brain" in i.lower() or "mouse" in i.lower()]
pathways_snc_vs_no, res_pathways_snc_vs_no = {}, {}
thresh_p_path = 0.05
# perts_geo = ["Aging", "Drug", "Disease"]
perts_geo = ["Aging", "Disease"]
other_sets = ["GTEx_Aging_Signatures_2021", f"HDSigDB_{species}_2021",
# "DGIdb_Drug_Targets_2024",
"MSigDB_Hallmark_2020",
"MSigDB_Oncogenic_Signatures", "OMIM_Disease",
# "Reactome_Pathways_2024", "Reactome_2022",
f"WikiPathways_2024_{species}", f"WikiPathways_2019_{species}"]
other_sets += [i for i in names if "GO_Biological_Process" in i]
# if species.lower() == "mouse":
# other_sets += ["PerturbAtlas_MouseGenePerturbationSigs"]
for x in df_degs.reset_index()[factor_name].unique():
pathways_snc_vs_no[x], res_pathways_snc_vs_no[x] = {}, {}
for i in ["up", "down"]:
degs_tmp = df_degs.loc[x]
if i == "up":
degs_tmp = list(degs_tmp[degs_tmp.log_fc > 0].index.values)
else:
degs_tmp = list(degs_tmp[degs_tmp.log_fc < 0].index.values)
if len(degs_tmp) > 0:
g_s = [f"{u}_Perturbations_from_GEO_{i}" for u in perts_geo] + [
f"Disease_Signatures_from_GEO_{i}_2014"] + other_sets
g_s = [g for g in g_s if g in names]
pathways_snc_vs_no[x][i] = gp.enrichr(
gene_list=degs_tmp, gene_sets=g_s,
organism=species, cutoff=0.5)
res_pathways_snc_vs_no[x][i] = pathways_snc_vs_no[x][i].results[
pathways_snc_vs_no[x][i].results[
"Adjusted P-value"] < thresh_p_path]
res_pathways_snc_vs_no[x] = pd.concat(res_pathways_snc_vs_no[x],
names=["Direction"])
res_pathways_snc_vs_no = pd.concat(res_pathways_snc_vs_no,
names=[col_celltype])
res_pathways_snc_vs_no.loc[
:, "Term Short"] = res_pathways_snc_vs_no.Term.apply(
lambda x: re.sub("Expression Of ", "", re.sub(".*.xls", "", re.sub(
".*.XLSX", "",
re.sub(".*Supplementary Table [0-9]+[-]?", "", re.sub(
".*.xlsx.", "", re.sub(".*Supplementary Data ..", "", re.sub(
"PMID([0-9])+", "", re.sub(
"([0-9]+) years v ([0-9]+) years", "\\1 v \\2 yrs.",
re.sub("aging:([0-9]+)", "aging", re.sub(
"([0-9]+) months v ([0-9]+) mo(nths)?",
"\\1 v \\2 mo.", re.sub("SRP[0-9]+ ", "", re.sub(
"GSE[0-9]+ ", "", x)))))))))))))
cts_i = res_pathways_snc_vs_no.reset_index()[col_celltype].unique()
res_pathways_snc_top_vs_no = res_pathways_snc_vs_no.reset_index().groupby([
col_celltype, "Direction"]).apply(lambda x: x.sort_values(
"Adjusted P-value", ascending=True).head(10), include_groups=False)
fig, axes = plt.subplots(*scflow.pl.square_grid(cts_i), figsize=(
20, 25), gridspec_kw=dict(wspace=1.5, top=0.95), squeeze=False)
for i, x in enumerate(cts_i):
gp.dotplot(
res_pathways_snc_top_vs_no.loc[x].reset_index(),
column="Adjusted P-value",
# column="Overlap",
x="Direction", y="Term Short",
ax=axes.flatten()[i], size=20, top_term=100,
title=x, xticklabels_rot=45, yticklabels_rot=45,
show_ring=True, marker="o", cutoff=0.5)
axes.flatten()[i].tick_params(axis="y", labelsize=8,
labelfontfamily="serif")
axes.flatten()[i].tick_params(axis="x", labelsize=10,
labelfontfamily="serif")
axes.flatten()[i].title.set_fontsize(10)
if len(axes.flatten()) > len(cts_i):
for a in axes.flatten()[len(cts_i):]:
a.set_visible(False)
fig.suptitle("Senescent Cell Spaceflight DEGs", fontsize="xx-large",
fontproperties=dict(family="serif"))
res_pathways_snc_top_vs_no[["Term Short", "Genes"] + list(
res_pathways_snc_top_vs_no.columns.difference([
"Term", "Genes", "Term Short"]))]
Ground Control | 29 Weeks
/home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms)) /home/easlinger/miniconda3/envs/rsc/lib/python3.13/site-packages/gseapy/plot.py:754: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning. .apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms))
Out[230]:
| Term Short | Genes | Adjusted P-value | Combined Score | Gene_set | Odds Ratio | Old Adjusted P-value | Old P-value | Overlap | P-value | level_2 | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| annotation_by_overlap | Direction | ||||||||||||
| Astrocyte | up | 1 | Top Astrocyte-Enriched Genes In Humans And Mice | LGI1;WDR17;DGKB;SGIP1;CTNND2;MMD2;FMN2;BCAN;TOX;SOX6;NCKAP5;BRINP3;RGS7 | 1.291089e-09 | 925.100860 | HDSigDB_Mouse_2021 | 33.752948 | 0 | 0 | 13/1055 | 1.249844e-12 | 258 |
| 2 | Genes Changed In Pvalb/Th-expressing Interneurons Of HD Patients Vs Control | LGI1;WDR17;DGKB;SGIP1;CTNND2;MMD2;FMN2;DAB1;SOX6;NCKAP5;BRINP3;CBR4;GAS8;RGS7 | 7.114723e-07 | 367.758962 | HDSigDB_Mouse_2021 | 18.024745 | 0 | 0 | 14/2304 | 1.377487e-09 | 259 | ||
| 3 | Genes Changed In Astrocytes Of HD Patients Vs Control | LGI1;WDR17;DGKB;SGIP1;MMD2;FMN2;BCAN;TOX;SOX6;NCKAP5;BRINP3;CBR4;RGS7 | 1.005231e-05 | 241.836387 | HDSigDB_Mouse_2021 | 13.939245 | 0 | 0 | 13/2362 | 2.919355e-08 | 260 | ||
| 4 | Genes Down-Regulated In Astrocytes Of HD Patients Vs Control | LGI1;WDR17;BCAN;SGIP1;MMD2;TOX;SOX6;NCKAP5;BRINP3;CBR4;RGS7 | 1.752029e-05 | 226.016634 | HDSigDB_Mouse_2021 | 13.879819 | 0 | 0 | 11/1628 | 8.472696e-08 | 261 | ||
| 6 | Genes Up-Regulated In Ciliated Ependymal Cells Of zQ175DN Vs WT | WDR17;DAB1;DGKB;CTNND2;SOX6;BRINP3 | 1.752029e-05 | 568.275792 | HDSigDB_Mouse_2021 | 35.399283 | 0 | 0 | 6/245 | 1.066926e-07 | 263 | ||
| 5 | Genes Up-Regulated In OPCs Of HD Patients Vs Control | DGKB;SGIP1;CTNND2;MMD2;FMN2;TOX;NCKAP5 | 1.752029e-05 | 418.683167 | HDSigDB_Mouse_2021 | 25.895105 | 0 | 0 | 7/414 | 9.509123e-08 | 262 | ||
| 7 | Genes Changed In Ciliated Ependymal Cells Of zQ175DN Vs WT | LGI1;WDR17;DAB1;DGKB;CTNND2;SOX6;BRINP3 | 1.752029e-05 | 396.091544 | HDSigDB_Mouse_2021 | 24.895254 | 0 | 0 | 7/430 | 1.230932e-07 | 264 | ||
| 8 | Genes Up-Regulated In Pvalb/Th-expressing Interneurons Of HD Patients Vs Control | LGI1;DAB1;SGIP1;CTNND2;MMD2;FMN2;SOX6;BRINP3 | 1.752029e-05 | 307.147712 | HDSigDB_Mouse_2021 | 19.423831 | 0 | 0 | 8/671 | 1.356848e-07 | 265 | ||
| 9 | Top OPC-enriched Genes In Humans And Mice | BCAN;DAB1;CTNND2;MMD2;FMN2;TOX;SOX6;NCKAP5;BRINP3 | 3.102987e-05 | 227.189098 | HDSigDB_Mouse_2021 | 15.022199 | 0 | 0 | 9/1041 | 2.703473e-07 | 266 | ||
| 10 | Genes Changed In Astrocytes Of R6/2 Vs WT | WDR17;BCAN;SGIP1;MMD2;NCKAP5 | 3.466799e-05 | 670.424264 | HDSigDB_Mouse_2021 | 44.972789 | 0 | 0 | 5/152 | 3.356049e-07 | 267 | ||
| Excitatory | up | 85 | Genes Down-Regulated In Sst/Npy-expressing Interneurons Of HD Patients Vs Control | PDIA3;RBM39;UVRAG;PTPN1;DYNC1H1;KCNJ6;UBE2E2;NRG1;DDX50;SCAF8;SIPA1L1;OLFM3;CPD;TET3;PHF24;ATXN7... | 1.119974e-05 | 338.279839 | HDSigDB_Mouse_2021 | 18.463768 | 0 | 0 | 17/4709 | 1.104510e-08 | 355 |
| 86 | Genes Down-Regulated In Foxp2/Olfm3-expressing Neurons Of HD Patients Vs Control | RBM39;SCAF8;SIPA1L1;OLFM3;DESI2;UBE2E2;NRG1;ATXN7L1;PAXBP1;FAM118B | 3.348232e-03 | 108.678502 | HDSigDB_Mouse_2021 | 9.111336 | 0 | 0 | 10/1986 | 6.604008e-06 | 356 | ||
| 87 | Genes Down-Regulated In Endothelial Cells Of R6/2 Vs WT | DESI2;CPD;NRG1;ATXN7L1 | 9.082970e-03 | 297.765344 | HDSigDB_Mouse_2021 | 28.292857 | 0 | 0 | 4/179 | 2.687269e-05 | 357 | ||
| 88 | Genes Changed In Endothelial Cells Of R6/2 Vs WT | DESI2;CPD;NRG1;ATXN7L1;DDX50 | 1.711809e-02 | 139.877012 | HDSigDB_Mouse_2021 | 14.565996 | 0 | 0 | 5/452 | 6.752698e-05 | 358 | ||
| 89 | Striatum RNA M14 Module (Cyan) | PDIA3;PTPN1;SCAF8;TET3;DDX50;FAM118B | 1.938115e-02 | 95.976560 | HDSigDB_Mouse_2021 | 10.369483 | 0 | 0 | 6/799 | 9.556780e-05 | 359 | ||
| 90 | Genes Down-Regulated In iSPNs Of HD Patients Vs Control | PDIA3;RBM39;PTPN1;DYNC1H1;SIPA1L1;OLFM3;DESI2;UBE2E2;NRG1;PAXBP1;DDX50 | 2.703019e-02 | 50.670152 | HDSigDB_Mouse_2021 | 5.797036 | 0 | 0 | 11/3490 | 1.599420e-04 | 360 | ||
| 91 | Genes Changed In iSPNs Of zQ175DN Vs WT | SIPA1L1;OLFM3;PHF24;NRG1;ATXN7L1 | 2.745761e-02 | 99.440407 | HDSigDB_Mouse_2021 | 11.602151 | 0 | 0 | 5/563 | 1.895496e-04 | 361 | ||
| 92 | Genes Changed In Foxp2/Olfm3-expressing Neurons Of HD Patients Vs Control | RBM39;KCNJ6;SCAF8;SIPA1L1;OLFM3;DESI2;UBE2E2;NRG1;ATXN7L1;PAXBP1;FAM118B | 2.909703e-02 | 43.530959 | HDSigDB_Mouse_2021 | 5.351129 | 0 | 0 | 11/3726 | 2.931251e-04 | 362 | ||
| 93 | Genes Down-Regulated In 4-Deoxypyridoxine Treated STHdhQ111 Vs Control GSE129143 | UVRAG;PTPN1;DYNC1H1;SCAF8;CPD;TET3 | 2.909703e-02 | 67.468227 | HDSigDB_Mouse_2021 | 8.317963 | 0 | 0 | 6/985 | 3.001741e-04 | 363 | ||
| 94 | Genes Changed In dSPNs Of zQ175DN Vs WT | SIPA1L1;OLFM3;PHF24;NRG1;ATXN7L1 | 2.909703e-02 | 84.819353 | HDSigDB_Mouse_2021 | 10.460832 | 0 | 0 | 5/622 | 3.010363e-04 | 364 | ||
| Excitatory-Inhibitory | up | 113 | Selective Autophagy (GO:0061912) | PEX5;ATG14;ATG4D | 8.355795e-03 | 676.244715 | GO_Biological_Process_2023 | 63.930481 | 0 | 0 | 3/58 | 2.547498e-05 | 1474 |
| 115 | Autophagosome Organization (GO:1905037) | TRAF6;ATG14;ATG4D | 8.393940e-03 | 535.407712 | GO_Biological_Process_2025 | 53.245989 | 0 | 0 | 3/69 | 4.295480e-05 | 1802 | ||
| 116 | Autophagosome Assembly (GO:0000045) | TRAF6;ATG14;ATG4D | 8.393940e-03 | 505.553005 | GO_Biological_Process_2025 | 50.923274 | 0 | 0 | 3/72 | 4.880198e-05 | 1803 | ||
| 110 | Genes Down-Regulated In Expression In The Heart Of Mice Inducibly Expressing Celf1 For 12 H GSE5... | APH1A;BAG6;ATP6V1G1;ARV1;TRAF6;PEX5;NCAPD2;B4GALNT1;ANKRD52;GIT1;HCFC1;ATG4D | 1.085353e-02 | 85.953510 | HDSigDB_Mouse_2021 | 7.645560 | 0 | 0 | 12/3289 | 1.310813e-05 | 247 | ||
| 112 | zellweger syndrome | PEX5 | 1.193737e-02 | 423.083052 | OMIM_Disease | 95.545455 | 0 | 0 | 1/12 | 1.193737e-02 | 1093 | ||
| 111 | Expression In The Heart Of Mice Inducibly Expressing Celf1 For 12 H GSE56185 | APH1A;BAG6;ATP6V1G1;ARV1;TRAF6;PEX5;NCAPD2;B4GALNT1;ANKRD52;GIT1;HCFC1;ATG4D | 1.309463e-02 | 71.587984 | HDSigDB_Mouse_2021 | 6.909091 | 0 | 0 | 12/3576 | 3.162955e-05 | 248 | ||
| 114 | Mitophagy (GO:0000423) | ATG14;ATG4D | 1.853978e-02 | 1440.040317 | GO_Biological_Process_2023 | 158.460317 | 0 | 0 | 2/16 | 1.130474e-04 | 1475 | ||
| 117 | Regulation of Cell Cycle Process (GO:0010564) | DCUN1D3;ZW10;GIT1 | 4.547639e-02 | 191.742222 | GO_Biological_Process_2025 | 24.480049 | 0 | 0 | 3/146 | 3.965964e-04 | 1804 | ||
| 118 | Macroautophagy (GO:0016236) | PEX5;ATG14;ATG4D | 4.621640e-02 | 165.624538 | GO_Biological_Process_2025 | 21.998890 | 0 | 0 | 3/162 | 5.374000e-04 | 1805 | ||
| Inhibitory | up | 121 | skeletal system development (GO:0001501) | TGFB2;NPR3;CREB3L2;HAPLN2 | 4.937853e-03 | 354.505843 | GO_Biological_Process_2021 | 32.185065 | 0 | 0 | 4/158 | 1.645951e-05 | 1303 |
| 120 | hypertension | NPR3 | 2.861721e-02 | 133.278878 | OMIM_Disease | 37.503759 | 0 | 0 | 1/29 | 2.861721e-02 | 1275 | ||
| 119 | Differentially Expressed Genes In Hypothalamus/Thalamus Of HD Q175 Mice | HHATL;ANO1;KCNJ10;NPR3;HAPLN2 | 3.241082e-02 | 169.588664 | HDSigDB_Mouse_2021 | 16.613232 | 0 | 0 | 5/398 | 3.687238e-05 | 341 | ||
| Microglial | up | 128 | Top Oligodendrocyte-Specific Genes In Humans And Mice | DOCK5;NRBP2;GPRC5B;GPR37;EFNB3;SCCPDH;WSCD1;DCTN2;PCDH9;SHISA4;ZFP365;PLS1 | 3.986842e-08 | 654.084342 | HDSigDB_Mouse_2021 | 27.234420 | 0 | 0 | 12/1055 | 3.712143e-11 | 319 |
| 129 | Top Oligodendrocyte-Enriched Genes In Humans And Mice | DOCK5;NRBP2;GPRC5B;GPR37;EFNB3;SCCPDH;WSCD1;PCDH9;SHISA4;ZFP365;PLS1 | 3.111173e-07 | 475.728744 | HDSigDB_Mouse_2021 | 22.625434 | 0 | 0 | 11/1035 | 7.385875e-10 | 320 | ||
| 130 | Top Oligodendrocyte-Expressed Genes In Humans And Mice | NRBP2;GPRC5B;GPR37;EFNB3;SCCPDH;WSCD1;DCTN2;PCDH9;KIF3A;ZFP365;COPE | 3.111173e-07 | 464.394062 | HDSigDB_Mouse_2021 | 22.258547 | 0 | 0 | 11/1051 | 8.690428e-10 | 321 | ||
| 131 | Mouse Cortex Mature Oligodendrocyte Markers | DOCK5;NRBP2;GPRC5B;GPR37;EFNB3;SCCPDH;WSCD1;SHISA4;COPE;PLS1;LRRC4B | 7.987744e-05 | 182.278061 | HDSigDB_Mouse_2021 | 12.129336 | 0 | 0 | 11/1840 | 2.974951e-07 | 322 | ||
| 132 | Transmembrane Receptors Encoding Genes Enriched In Myelinating Oligodendrocytes | GPRC5B;GPR37;EFNB3 | 1.237809e-04 | 3615.701150 | HDSigDB_Mouse_2021 | 251.672269 | 0 | 0 | 3/17 | 5.762613e-07 | 323 | ||
| 133 | Mouse Cerebellum Mature Oligodendrocyte Markers | DOCK5;GPRC5B;GPR37;EFNB3;SCCPDH;WSCD1;SHISA4;PLS1;LRRC4B | 2.605618e-04 | 163.098887 | HDSigDB_Mouse_2021 | 12.135283 | 0 | 0 | 9/1271 | 1.455653e-06 | 324 | ||
| 168 | KRAS.KIDNEY UP.V1 UP | GPRC5B;GPR37;WSCD1;PCDH9 | 3.754310e-04 | 399.353853 | MSigDB_Oncogenic_Signatures | 35.175532 | 0 | 0 | 4/145 | 1.173222e-05 | 1396 | ||
| 134 | Genes Up-Regulated In The Astrocytes Of zQ175 KI Mice Vs WT At 12 Month Old GSE124846 | BSCL2;DOCK5;NRBP2;SCCPDH;WSCD1;PCDH9;PLS1 | 8.434368e-04 | 166.978595 | HDSigDB_Mouse_2021 | 13.787053 | 0 | 0 | 7/758 | 5.497261e-06 | 325 | ||
| 135 | Striatum RNA M47 Module (Bisque4) | DOCK5;GPR37;WSCD1;SHISA4 | 8.751972e-04 | 489.941434 | HDSigDB_Mouse_2021 | 41.030992 | 0 | 0 | 4/125 | 6.519160e-06 | 326 | ||
| 136 | The Global Proteome Data Of The Soluble Fraction From The Brains Of Q175 And WT Mice | DOCK5;GPR37;DCTN2;SHISA4;NRBP2;GPRC5B;EFNB3;SCCPDH;KIF3A;MPC2;COPE;PLS1;LRRC4B | 1.341768e-03 | 87.593047 | HDSigDB_Mouse_2021 | 7.686508 | 0 | 0 | 13/3901 | 1.124386e-05 | 327 | ||
| OPC | up | 173 | spastic paraplegia | PNPLA6 | 3.153280e-02 | 117.076820 | OMIM_Disease | 33.869270 | 0 | 0 | 1/32 | 3.153280e-02 | 1173 |
| 172 | SIRNA EIF4GI DN | DCTN6;SRPK1 | 4.783250e-02 | 123.851850 | MSigDB_Oncogenic_Signatures | 22.775487 | 0 | 0 | 2/99 | 4.348409e-03 | 1162 | ||
| Oligodendrocyte | up | 174 | Genes Down-Regulated In Myotonic Dystrophy 2 Markers In Human Skeletal Muscle E-D-7014 | DPAGT1;CENPX;TST;9130401M01RIK;SLC35C2;EPHX1;FUZ;CRIP2;SMCR8;RFNG;APTX;LY6E | 1.309332e-02 | 86.403136 | HDSigDB_Mouse_2021 | 7.667941 | 0 | 0 | 12/3281 | 1.277397e-05 | 447 |
| 177 | Aflatoxin B1 Metabolism WP1262 | EPHX1 | 1.991450e-02 | 1393.123889 | WikiPathways_2024_Mouse | 262.842105 | 0 | 0 | 1/5 | 4.990441e-03 | 1516 | ||
| 179 | Aflatoxin B1 metabolism WP1262 | EPHX1 | 1.991450e-02 | 1393.123889 | WikiPathways_2019_Mouse | 262.842105 | 0 | 0 | 1/5 | 4.990441e-03 | 1520 | ||
| 178 | Fatty Acid Oxidation WP2318 | SLC25A29 | 1.991450e-02 | 538.335775 | WikiPathways_2024_Mouse | 116.789474 | 0 | 0 | 1/10 | 9.957248e-03 | 1517 | ||
| 180 | Fatty acid oxidation WP2318 | SLC25A29 | 1.991450e-02 | 538.335775 | WikiPathways_2019_Mouse | 116.789474 | 0 | 0 | 1/10 | 9.957248e-03 | 1521 | ||
| 175 | VEGF A UP.V1 UP | EPHX1;RFNG;LY6E | 3.083421e-02 | 126.205684 | MSigDB_Oncogenic_Signatures | 18.092350 | 0 | 0 | 3/196 | 9.343700e-04 | 1482 | ||
| 176 | STK33 NOMO DN | DPAGT1;VAT1;SLC35C2 | 4.819711e-02 | 70.168775 | MSigDB_Oncogenic_Signatures | 12.023814 | 0 | 0 | 3/292 | 2.921037e-03 | 1483 | ||
| Overall | up | 233 | Negative Regulation Of Peptidase Activity (GO:0010466) | SERPINE2;TIMP2;NRG1;CTSB | 9.062129e-05 | 1324.283459 | GO_Biological_Process_2023 | 88.946429 | 0 | 0 | 4/60 | 3.419671e-07 | 2017 |
| 234 | Negative Regulation Of Proteolysis (GO:0045861) | SERPINE2;TIMP2;CTSB | 1.178829e-03 | 1077.036200 | GO_Biological_Process_2023 | 92.609907 | 0 | 0 | 3/41 | 8.896824e-06 | 2018 | ||
| 236 | Negative Regulation Of Endopeptidase Activity (GO:0010951) | SERPINE2;TIMP2;NRG1 | 1.897170e-03 | 568.434094 | GO_Biological_Process_2023 | 55.789916 | 0 | 0 | 3/66 | 3.758775e-05 | 2020 | ||
| 235 | Negative Regulation Of Platelet Aggregation (GO:0090331) | SERPINE2;CD9 | 1.897170e-03 | 3897.964407 | GO_Biological_Process_2023 | 369.888889 | 0 | 0 | 2/8 | 2.650430e-05 | 2019 | ||
| 238 | Regulation Of Endopeptidase Activity (GO:0052548) | SERPINE2;TIMP2;NRG1 | 1.897170e-03 | 535.407712 | GO_Biological_Process_2023 | 53.245989 | 0 | 0 | 3/69 | 4.295480e-05 | 2022 | ||
| 237 | Negative Regulation Of Homotypic Cell-Cell Adhesion (GO:0034111) | SERPINE2;CD9 | 1.897170e-03 | 2791.902845 | GO_Biological_Process_2023 | 277.388889 | 0 | 0 | 2/10 | 4.254530e-05 | 2021 | ||
| 239 | Negative Regulation Of Platelet Activation (GO:0010544) | SERPINE2;CD9 | 3.746939e-03 | 1573.576348 | GO_Biological_Process_2023 | 170.658120 | 0 | 0 | 2/15 | 9.897575e-05 | 2023 | ||
| 240 | Heart Trabecula Morphogenesis (GO:0061384) | S1PR1;NRG1 | 4.768772e-03 | 1226.393330 | GO_Biological_Process_2023 | 138.638889 | 0 | 0 | 2/18 | 1.439629e-04 | 2024 | ||
| 201 | Status Epilepticus C0038220 rat sample 391 | SERPINE2;VGF;TIMP2;CD9;CTSB | 5.809842e-03 | 218.064714 | Disease_Perturbations_from_GEO_up | 19.787513 | 0 | 0 | 5/336 | 1.636575e-05 | 146 | ||
| 243 | Negative Regulation of Platelet Aggregation (GO:0090331) | SERPINE2;CD9 | 5.846481e-03 | 3261.459893 | GO_Biological_Process_2025 | 317.031746 | 0 | 0 | 2/9 | 3.405660e-05 | 2282 |
